Intelligent Data Storage and Processing Using FPGA Devices

ABSTRACT

A re-configurable logic device such as a field programmable gate array (FPGA) can be used to deploy a data processing pipeline, the pipeline comprising a plurality of pipelined data processing engines, the plurality of pipelined data processing engines including a data reduction engine, the plurality of pipelined data processing engines being configured to perform processing operations, wherein the pipeline comprises a multi-functional pipeline, and wherein the re-configurable logic device is further configured to controllably activate or deactivate each of the pipelined data processing engines in the pipeline in response to control instructions and thereby define a function for the pipeline, each pipeline function being the combined functionality of each activated pipelined data processing engine in the pipeline.

CROSS REFERENCE AND PRIORITY CLAIM TO RELATED APPLICATIONS

This application is a continuation of patent application Ser. No.10/550,323, entitled “Intelligent Data Storage and Processing Using FPGADevices”, now U.S. Pat. No. ______, which is a national stage entry ofPCT patent application PCT/US04/16398, entitled “Intelligent DataStorage and Processing Using FPGA Devices”, filed May 21, 2004, whichclaims the benefit of provisional patent application Ser. No. 60/473,077entitled “Intelligent Data Storage and Processing”, filed May 23, 2003,the entire disclosures of each of which are incorporated herein byreference.

This application is related to patent application Ser. No. 13/165,155and patent application Ser. No. 10/550,326, both entitled “IntelligentData Storage and Processing Using FPGA Devices”, where the Ser. No.13/165,155 patent application is a divisional of the Ser. No. 10/550,326patent application and where the Ser. No. 10/550,326 patent applicationis a national stage entry of PCT patent application PCT/US04/16021,entitled “Intelligent Data Storage and Processing Using FPGA Devices”,filed May 21, 2004, which claims the benefit of provisional patentapplication Ser. No. 60/473,077 entitled “Intelligent Data Storage andProcessing”, filed May 23, 2003.

This application is also related to patent application Ser. No.10/153,151 entitled “Associative Database Scanning and InformationRetrieval Using FPGA Devices”, filed May 21, 2002, now U.S. Pat. No.7,139,743, which is a continuation-in-part of patent application Ser.No. 09/545,472 entitled “Associative Database Scanning and InformationRetrieval”, filed Apr. 7, 2000, now U.S. Pat. No. 6,711,558, the entiredisclosures of both of which are incorporated herein by reference.

This patent application is also related to patent application Ser. No.______, entitled “Intelligent Data Storage and Processing Using FPGADevices”, filed this same day (and identified by Thompson CoburnAttorney Docket 53047-99897).

BACKGROUND AND SUMMARY OF THE INVENTION

Indications are that the average database size and associated softwaresupport systems are growing at rates that are greater than the increasein processor performance (i.e., more than doubling roughly every 18months). This is due to a number of factors including without limitationthe desire to store more detailed information, to store information overlonger periods of time, to merge databases from disparate organizations,and to deal with the large new databases which have arisen from emergingand important applications. For example, two emerging applicationshaving large and rapidly growing databases are those connected with thegenetics revolution and those associated with cataloging and accessinginformation on the Internet. In the case of the Internet, currentindustry estimates are that in excess of 1.5 million pages are added tothe Internet each day. At the physical level this has been made possibleby the remarkable growth in disk storage performance where magneticstorage density has been doubling every year or so for the past fiveyears.

Search and retrieval functions are more easily performed on informationwhen it is indexed. For example, with respect to financial information,it can be indexed by company name, stock symbol and price. Oftentimes,however, the information being searched is of a type that is either hardto categorize or index or which falls into multiple categories. As aresult, the accuracy of a search for information is only as good as theaccuracy and comprehensiveness of the index created therefor. In thecase of the Internet, however, the information is not indexed. Thebottleneck for indexing is the time taken to develop the reverse indexneeded to access web pages in reasonable time. For example, while thereare search engines available, designing a search which will yield amanageable result is becoming increasingly difficult due to the largenumber of “hits” generated by less than a very detailed set of searchinstructions. For this reason, several “intelligent” search engines havebeen offered on the web, such as Google, which are intended to whittledown the search result using logic to eliminate presumed undesired“hits”.

With the next-generation Internet, ever-faster networks, and expansionof the Internet content, this bottleneck is becoming a critical concern.Further, it is becomingly exceedingly difficult to index information ona timely basis. In the case of the Internet, current industry estimatesare that in excess of 1.5 million pages are added to the Internet eachday. As a result, maintaining and updating a reverse index has become anenormous and continuous task and the bottleneck it causes is becoming amajor impediment to the speed and accuracy of existing search andretrieval systems. Given the ever increasing amounts of informationavailable, however, the ability to accurately and quickly search andretrieve desired information has become critical.

Associative memory devices for dealing with large databases are known inthe prior art. Generally, these associative memory devices compriseperipheral memories for computers, computer networks, and the like,which operate asynchronously to the computer, network, etc. and provideincreased efficiency for specialized searches. Additionally, it is alsoknown in the prior art that these memory devices can include certainlimited decision-making logic as an aid to a main CPU in accessing theperipheral memory. An example of such an associative memory deviceparticularly adapted for use with a rotating memory such as a high speeddisk or drum can be found in U.S. Pat. No. 3,906,455, the disclosure ofwhich is incorporated herein by reference. This particular deviceprovides a scheme for use with a rotating memory and teaches that twopasses over a memory sector is necessary to presort and then sort thememory prior to performing any logical operations thereon. Thus, thisdevice is taught as not being suitable for use with any linear or serialmemory such as magnetic tape or the like.

Other examples of prior art devices may also be found in U.S. Pat. Nos.3,729,712; 4,464,718; 5,050,075; 5,140,692; and 5,721,898; thedisclosures of which are incorporated herein by reference.

As an example, in U.S. Pat. No. 4,464,718, Dixon performs fixedcomparisons on a fixed number of bytes. They don't have the ability toscan and correlate arbitrarily over the data. They search serially alongthe tracks in a given disk cylinder but there is no provision forparallel searching across disks. Dixon's comparisons are limited by afixed rigid number of standard logical operation types. Additionally,the circuitry presented supports only these single logical operations.There is no support for approximate or fuzzy matching.

While these prior art associative memory devices represent an attempt tospeed the input and output of information to and from a peripheralmemory, which in many cases is a mass storage memory device, all rely onthe classic accessing of data stored in digital form by reading andinterpreting the digital either address or content of the memorylocation. In other words, most such devices access data by its addressbut there are some devices that take advantage of the power of contentaddressing as is well known in the art. Nevertheless, in all of theprior art known to the inventors, the digital value of the address ordata contained in the addressed location must be read and interpreted inits digital form in order to identify the data and then select it forprocessing. Not only does it take processing time to read and interpretthe digital data represented by the address or content, this necessarilyrequires that the accessing circuit process the memory according to thestructure of the data stored. In other words, if the data is stored inoctets, then the accessing circuitry must access the data in octets andprocess it in an incremental manner. This “start and stop” processingserves to increase the input/output time required to access data. As isalso well known in the art, this input/output time typically representsthe bottleneck and effective limitation of processing power in anycomputer or computer network.

Furthermore, given the vast amount of information available to besearched, data reduction and classification operations (e.g., theability to summarize data in some aggregate form) has become critical.Oftentimes, the ability to quickly perform data reduction functions canprovide a company with a significant competitive advantage.

Likewise, with the improvements in digital imaging technology, theability to perform two dimensional matching such as on images has becomenecessary. For example, the ability to conduct matches on a particularimage of an individual, such as his or her face or retina, or on afingerprint, is becoming critical to law enforcement as it steps up itsefforts on security in light of the Sep. 11, 2001 terrorist attacks.Image matching is also of importance to the military in the area ofautomatic target recognition.

Finally, existing searching devices cannot currently be quickly andeasily reconfigured in response to changing application demands.

Accordingly, there is a need for an improved information search andretrieval system and method which overcomes these and other problems inthe prior art.

As described in parent application Ser. No. 10/153,151, in order tosolve these and other problems in the prior art, inventors herein havesucceeded in designing and developing a method and apparatus for anassociative memory using Field Programmable Gate Arrays (FPGA) inseveral embodiments which provide an elegantly simple solution to theseprior art limitations as well as dramatically decreased access times fordata stored in mass storage memories. As described therein, theinvention of the Ser. No. 10/153,151 patent application has severalembodiments each of which has its own advantages. Grandparent patentapplication Ser. No. 09/545,472, now U.S. Pat. No. 6,711,558, disclosesand claims the use of programmable logic and circuitry generally withoutbeing specific as to any choice between the various kinds of devicesavailable for this part of the invention. In the Ser. No. 10/153,151application, the inventors disclosed more specifically the use of FPGA'sas part of the circuitry for various reasons as their best mode. Animportant reason amongst others is speed. And, there are two differentaspects of operation in which speed plays a part. The first of these isthe speed of reconfiguration. It is known in the art that FPGA's may bequickly programmed in the field to optimize the search methodology usinga template, the template having been prepared in advance and merelycommunicated to the FPGA's over a connecting bus. Should it then bedesired to search using a different methodology, the FPGA's may then bequickly and conveniently re-programmed with another prepared template ina minimal number of clock cycles and the second search startedimmediately. Thus, with FPGA's as the re-configurable logic, shiftingfrom one search to another is quite easy and quick, relative to othertypes of re-programmable logic devices.

A second aspect of speed is the amount of time, once programmed, that asearch requires. As FPGA's are hardware devices, searching is done athardware processing speeds which is orders of magnitude faster than atsoftware processing speeds as would be experienced with amicroprocessor, for example. Thus, FPGA's are desirable over othersoftware implementations where speed is a consideration as it most oftenis.

In considering the use of templates, the Ser. No. 10/153,151 applicationdiscloses that at least several “generic” templates can be prepared inadvance and made available for use in performing text searching ineither an absolute search, an approximate search, or a higher oradvanced search mode incorporating a Boolean algebra logic capability,or a graphics search mode. These could then be stored in a CPU memoryand be available either on command or loaded in automatically inresponse to a software queue indicating one of these searches.

Still another factor to consider is cost, and the recent pricereductions in FPGA's have made them more feasible for implementation asa preferred embodiment for this application, especially as part of ahard disk drive accelerator as would be targeted for a pc market. It isfully expected that further cost reductions will add to the desirabilityof these for this implementation, as well as others as discussed ingreater detail below.

Generally, the invention of the Ser. No. 10/153,151 application may bedescribed as a technique for data retrieval through approximate matchingof a data key with a continuous reading of data as stored on a massstorage medium, using FPGA's to contain the template for the search anddo the comparison, all in hardware and at essentially line speed. Byutilizing FPGA's, the many advantages and features commonly known aremade available. These include the ability to arrange the FPGA's in a“pipeline” orientation, in a “parallel” orientation, or even in an arrayincorporating a complex web overlay of interconnecting data pathsallowing for complex searching algorithms. In its broadest, and perhapsmost powerful, embodiment, the data key may be an analog signal and itis matched with an analog signal generated by a typical read/writedevice as it slews across the mass storage medium. In other words, thesteps taught to be required in the prior art of not only reading theanalog representation of digital data stored on the mass storage mediumbut also the conversion of that signal to its digital format prior tobeing compared are eliminated. Furthermore, there is no requirement thatthe data be “framed” or compared utilizing the structure or format inwhich the data has been organized and stored. For an analog signal, allthat need be specified is the elapsed time of that signal which is usedfor comparison with a corresponding and continuously changing selectedtime portion of the “read” signal. Using any one of many standardcorrelation techniques as known in the prior art, the data “key” maythen be approximately matched to the sliding “window” of data signal todetermine a match. Significantly, the same amount of data may be scannedmuch more quickly and data matching the search request may be determinedmuch more quickly as well. For example, the inventors have found thatCPU based approximate searches of 200 megabytes of DNA sequences cantake up to 10 seconds on a typical present day “high end” system,assuming the offline processing to index the database has already beencompleted. In that same 10 seconds, the inventors have found that a10-gigabyte disk could be searched for approximate matches using thepresent invention. This represents a 50:1 improvement in performance.Furthermore, in a typical hard disk drive there are four surfaces andcorresponding read/write heads, which may be all searched in parallelshould each head be equipped with the present invention. As thesesearches can proceed in parallel, the total increase in speed orimprovement represents a 200:1 advantage. Furthermore, additional harddisk drives may be accessed in parallel and scaled to further increasethis speed advantage over conventional systems.

By choosing an appropriate correlation or matching technique, and bysetting an appropriate threshold, the search may be conducted to exactlymatch the desired signal, or more importantly and perhaps morepowerfully, the threshold may be lowered to provide for approximatematching searches. This is generally considered a more powerful searchmode in that databases may be scanned to find “hits” which may be valideven though the data may be only approximately that which is beingsought. This allows searching to find data that has been corrupted,incorrectly entered data, data which only generally corresponds to acategory, as well as other kinds of data searches that are highlydesired in many applications. For example, a library of DNA sequencesmay be desired to be searched and hits found which represent anapproximate match to a desired sequence of residues. This ensures thatsequences which are close to the desired sequence are found and notdiscarded but for the difference in a forgivable number of residuemismatches. Given the ever-increasing volume and type of informationdesired to be searched, more complex searching techniques are needed.This is especially true in the area of molecular biology, “[O]ne of themost powerful methods for inferring the biological function of a gene(or the protein that it encodes) is by sequence similarity searching onprotein and DNA sequence databases.” Garfield, “The Importance of(Sub)sequence Comparison in Molecular Biology,” pgs. 212-217, thedisclosure of which is incorporated herein by reference. Currentsolutions for sequence matching are only available in software ornon-reconfigurable hardware.

Still another application involves Internet searches provided byInternet search engines. In such a search, approximate matching allowsfor misspelled words, differently spelled words, and other variations tobe accommodated without defeating a search or requiring a combinatorialnumber of specialized searches. This technique permits a search engineto provide a greater number of hits for any given search and ensure thata greater number of relevant web pages are found and cataloged in thesearch. Although, as mentioned above, this approximate matching casts awider net which produces a greater number of “hits” which itself createsits own problems.

Still another possible application for this inventive technology is foraccessing databases which may be enormous in size or which may be storedas analog representations. For example, our society has seen theimplementation of sound recording devices and their use in many forumsincluding judicial proceedings. In recent history, tape recordings madein the President's oval office have risen in importance with respect toimpeachment hearings. As can be appreciated, tape recordings made overthe years of a presidency can accumulate into a huge database whichmight require a number of persons to actually listen to them in order tofind instances where particular words are spoken that might be ofinterest. Utilizing this inventive technology, an analog representationof that spoken word can be used as a key and sought to be matched whilethe database is scanned in a continuous manner and at rapid speed. Thus,the present and parent inventions provide a powerful search tool formassive analog databases as well as massive digital databases.

While text-based searches are accommodated by the present and parentinventions as described above, storage media containing images, sound,and other representations have traditionally been more difficult tosearch than text. The present and parent inventions allow searching alarge data base for the presence of such content or fragments thereof.For example, the key in this case could be a row or quadrant of pixelsthat represent the image being sought. Approximate matching of the key'ssignal can then allow identification of matches or near matches to thekey. In still another image application, differences in pixels or groupsof pixels can be searched and noted as results which can be importantfor satellite imaging where comparisons between images of the samegeographic location are of interest as indicative of movement ofequipment or troops.

The present and parent inventions may be embodied in any of severalconfigurations, as is noted more particularly below. However, oneimportant embodiment is perhaps in the form of a disk drive acceleratorwhich would be readily installed in any PC as an interface between thehard disk drive and the system bus. This disk drive accelerator could beprovided with a set of standardized templates and would provide a “plugand play” solution for dramatically increasing the speed at which datacould be accessed from the drive by the CPU. This would be an aftermarket or retrofit device to be sold to the large installed base ofPC's. It could also be provided as part of a new disk drive, packagedwithin the envelope of the drive case or enclosure for an external driveor provided as an additional plug in pc card as an adapter for aninternal drive. Additional templates for various kinds of searches onvarious kinds of databases could be made available either with thepurchase of the accelerator, such as by being encoded on a CD, or evenover the Internet for download, as desired.

The present invention extends the novel groundbreaking technologydisclosed in the parent application Ser. Nos. 09/545,472 and 10/153,151such that a programmable logic device (PLD) such as an FPGA performs anyof a variety of additional processing operations including but notlimited to operations such as encryption, decryption, compression, anddecompression. Thus, the technology of the parent applications has beenextended such that PLDs perform data manipulation operations. As usedherein, the term “manipulating” or “manipulation” refers to theperformance of a search operation, a reduction operation, or aclassification operation on data in combination with any or all of acompression operation, a decompression operation, an encryptionoperation, and a decryption operation also performed on the data, or theperformance of a compression operation or a decompression operation ondata alone or in combination with any or all of a search operation, areduction operation, a classification operation, an encryptionoperation, and a decryption operation also performed on the data. Notonly can these manipulation operations be performed at very high speedsdue to the inventive techniques disclosed herein, but these operations,when implemented on a PLD such as an FPGA as disclosed herein alsoenhance data security by protecting the unencrypted and/or decompresseddata from being accessed or read by any viruses or malware that may berunning in the software of the computer system and using there-configurable logic to process stored data. Among the more powerfulapplications for the present invention is to perform high speed searcheswithin encrypted data, which can be referred to as crypto-searching.With crypto-searching, the stream of encrypted data is processed tofirst decrypt the data stream and then perform a search operation withinthe decrypted data.

The value of data security to data owners cannot be underestimated andis ever-increasing in importance, and the ability to control who hasaccess to what data and when lies at the heart of data security. Amongits many unique applications, the present invention provides flexibilityto data owners in controlling who has access to their data, and speed inproviding authorized users with access to that data (or targeted accessto a portion of that data through scanning capabilities).

Further still, the use of compression and/or decompression as describedherein allows data to be stored in a manner that takes up less space inthe mass storage medium, while still retaining the ability to searchthrough the data at high speeds.

Preferably, these manipulation operations, when implemented withmultiple stages, are implemented in a pipelined manner. In particular,the combination of one or more stages dedicated to encryption/decryptionor compression/decompression with one or more stages dedicated to datasearching or data reduction synergistically produces an intelligent,flexible, high speed, and secure design technique for data storage andretrieval.

Further still, disclosed herein is a novel and unique technique forstoring data on a magnetic medium such as a computer hard disk so thatlarge amounts of data can be read therefrom without being significantlydisadvantaged by the disk storage system's “seek” times. In accordancewith this feature of the invention, data is stored on the magneticmedium as a plurality of discontiguous arcs positioned on the magneticmedium, preferably in a helical or spiral pattern. When a systememploying a PLD for searching and/or additional processing, as describedherein, is used in combination with a mass storage medium that employsdata stored in a piecewise helical fashion, as described herein, thiscombination synergistically results in ever greater processing speeds.

Further still, a novel technique for storing data files in memory isdisclosed herein, wherein a data file is stored using a sum of powers of2 technique. The combination of data file storage using this sum ofpowers of 2 technique with the data processing capabilities of there-configurable logic platform described herein also synergisticallyresults in enhanced processing speeds.

While the principal advantages and features of the present inventionhave been briefly explained above, a more thorough understanding of theinvention may be attained by referring to the drawings and descriptionof the preferred embodiment which follow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an information search andretrieval system in accordance with one embodiment;

FIG. 2 is a schematic of a conventional rigid disk drive systemillustrating different insertion points for connection of the inventivesystem;

FIG. 3 is a block diagram of one embodiment of the transformation of asearch inquiry processed by the system of FIG. 1;

FIG. 4 is a block diagram of one embodiment of a hardware implementationused to conduct an exact match search in a digital domain;

FIG. 5 is a block diagram of one embodiment of a hardware implementationused to conduct an approximate match search in a digital domain;

FIG. 6 is a block diagram depicting the implementation of the inventivesystem in a stand-alone configuration;

FIG. 7 is a block diagram depicting an inventive implementation as ashared remote mass storage device across a network;

FIG. 8 is a block diagram depicting an inventive implementation as anetwork attached storage device (NASD);

FIG. 9 is a flowchart detailing the logical steps for searching andretrieving data from a magnetic storage medium;

FIG. 10 is a graphical representation of an analog signal as might beused as a data key;

FIG. 11 is a graphical representation of an analog signal representingthe continuous reading of data from a magnetic storage medium in whichthe data key is present;

FIG. 12 is a graphical representation of the signal of FIG. 10 overlyingand matched to the signal of FIG. 11;

FIG. 13 is a graphical representation of a correlation functioncalculated continuously as the target data in the magnetic storagemedium is scanned and compared with the data key;

FIG. 14 is a graphical representation of a correlation function as thedata key is continuously compared with a signal taken from reading adifferent set of target data from the magnetic storage medium but whichalso contains the data key;

FIG. 15 is one embodiment of a table generated by the present inventionfor use in performing sequence matching operations;

FIG. 16 is a block diagram of one embodiment of a systolic arrayarchitecture that can be used by the inventive system to compute thevalues of the table of FIG. 15;

FIGS. 17 and 18 are block diagrams of the systolic array architecture ofFIG. 15 in operation during the combinatorial and latch part of theclock cycle, respectively, of the system of FIG. 1;

FIG. 19 is the table of FIG. 15 representing a particular sequencematching example;

FIG. 20 is a block diagram of the systolic array architecture of FIG. 16for the example of FIG. 19;

FIGS. 20, 21 and 22 are block diagrams of the systolic arrayarchitecture of FIG. 20 in operation during the combinatorial and latchpart of the clock cycle, respectively, of the system of FIG. 1;

FIG. 23 is a block diagram of one embodiment of a systolic arrayarchitecture that can be used by the inventive system in performingimage matching operations;

FIG. 24 is a block diagram of another arrangement for the systolic arrayarchitecture in performing image matching operations;

FIG. 25 is a block diagram of one embodiment of an individual cell ofthe systolic array shown in FIG. 23;

FIG. 26 is a block diagram of another embodiment of an individual cellof the systolic array shown in FIG. 23;

FIG. 27 is a block diagram showing an example using the inventive systemfor performing data reduction operations; and

FIG. 28 is a block diagram showing a more complex arrangement of FPGA's;

FIGS. 29 and 30 illustrate exemplary embodiments for multi-stageprocessing pipelines implemented on a re-configurable logic device;

FIG. 31 illustrates an encryption engine implemented on are-configurable logic device;

FIG. 32 illustrates another exemplary embodiment for a multi-stageprocessing pipeline implemented on a re-configurable logic device;

FIGS. 33-35 illustrate various encryption engines that can beimplemented on re-configurable logic;

FIG. 36 illustrates a three party data warehousing scenario;

FIG. 37 illustrates a non-secure data warehousing decryption scenario;

FIGS. 38-39( b) illustrate various exemplary embodiments for secure datadelivery in a data warehousing scenario;

FIGS. 40-42 illustrate various exemplary embodiments for implementingcompression and/or decompression on a re-configurable logic device;

FIG. 43 depicts a process flow for creating a template to be loaded ontoa re-configurable logic device;

FIGS. 44( a) and (b) illustrate a conventional hard disk using circulartracks and a disk drive system for use therewith;

FIG. 45 illustrates a novel planar magnetic medium having discretecircular arcs arranged in a helical pattern;

FIG. 46 illustrates a head positioning flow for reading data from themagnetic medium of FIG. 45; and

FIGS. 47( a) and (b) illustrate two embodiments of a sum of powers of 2file system;

FIGS. 48-50 plot various performance characteristics for a sum of powersof 2 file system.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

As shown in FIG. 1, the present invention is readily implemented in astand-alone computer or computer system. In broad terms, the inventionis comprised of at least one re-configurable logic device 21 coupled toat least one magnetic mass storage medium 26, with that re-configurablelogic device being an FPGA. As depicted in FIG. 1, the re-configurablelogic device 21 may itself include a plurality of functional logicelements including a data shift register and possibly a microprocessor,or they could be on separate chips, or the individual logic elementscould be configured in a pipeline or parallel orientation as shown insome of the other figures herein. In any event, re-configurable logicrefers to any logic technology whose form and function can besignificantly altered (i.e., reconfigured) in the fieldpost-manufacture. Examples of re-configurable logic devices includewithout limitation programmable logic devices (PLDs). A PLD is anumbrella term for a variety of chips that are programmable. There aregenerally three physical structures for a PLD. The first is thepermanent fuse type which blows apart lines or fuses them together byelectrically melting an aluminum trace or insulator. This was the firsttype of PLD, known as a “programmable array logic” or PAL. The secondtype of PLD uses EEPROM or flash memory, and causes a transistor to openor close depending on the contents of its associated memory cell. Thethird type of PLD is RAM-based (which makes it dynamic and volatile),and its contents are loaded each time it starts up. An FPGA is anintegrated circuit (IC) that contains an array of logic units that canbe interconnected in an arbitrary manner. These logic units are referredto as CLB's or configurable logic blocks by one vendor (Xilinx). Boththe specific function of each logic unit and the interconnectionsbetween logic units can be programmed in the field after manufacture ofthe IC. FPGAs are one of the most common PLD chips. FPGAs are availablein all three structures. The box labeled in FIG. 1 for reconfigurablelogic device 21 is meant to convey that not only can the task performedby reconfigurable logic device 20 be implemented in reconfigurablehardware logic, but the tasks of the data shift register 24 and/orcontrol microprocessor 22 may also optionally be implemented in thereconfigurable hardware logic of reconfigurable logic device 21. In thepreferred embodiment of the present invention, re-configurable logicdevice 21 is constructed using Xilinx FPGA technology, and itsconfiguration is developed using the Mentor synthesis tools orSynplicity synthesis tools and the Xilinx place-and-route tools, all ofwhich are presently commercially available as known to those of skill inthe art.

The re-configurable logic device 21 interfaces with the system orinput/output bus 34 and, in one configuration, also interfaces with anydisk caches 30 which may be present. It receives and processes searchrequests or inquires from the CPU 32 or network interface 36.Additionally, the device may aid in passing the results of the inquiriesto either or both the disk cache 30 and/or the CPU 32 (by way of the bus34).

The mass storage medium 26 provides the medium for storing large amountsof information which will hereafter be referred to as target data. Theterm “mass storage medium” should be understood as meaning any deviceused to store large amounts of data, and which is typically designatedfor use in a computer or computer network. Examples include withoutlimitation hard disk drives, optical storage media, or sub-units such asa single disk surface, and these systems may be rotating, linear,serial, parallel, or various combinations of each. For example, a rackof hard disk drive units could be connected in parallel and theirparallel output provided at the transducer level to one or morere-configurable logic devices 21. Similarly, a bank of magnetic tapedrives could be used, and their serial outputs each provided in parallelto one or more re-configurable logic devices 21. The data stored on themedium may be in analog or in digital form. For example, the data couldbe voice recordings. The invention is thus scalable, permitting anincrease in the amount of data stored by increasing the number ofparallel mass storage media, while preserving the performance byincreasing the number of parallel re-configurable logic devices orreplicating the re-configurable logic device.

In the prior art as shown in the upper portion of FIG. 1, typically adisk controller 28 and/or a disk cache 30 may be used in the traditionalsense for access by a CPU 32 over its system or input/output bus 34. There-configurable logic device 21 accesses target data in the mass storagemedium 26 via one or more data shift registers 24 and presents it foruse at the system bus 34 without moving large blocks of memory from themass storage medium 26 over the system bus 34 and into the workingmemory 33 of CPU 32 for sorting and accessing. In other words, as isexplained in greater detail below, the CPU 32 may send a search requestor inquiry to the re-configurable logic device 21 which thenasynchronously accesses and sorts target data in the mass storage medium26 and presents it for use either in a disk cache 30 as is known in theprior art or directly onto the system bus 34 without further processingbeing required by CPU 32 or use of its working memory 33. The CPU 32 isthus free to perform other tasks while the searching and matchingactivity is being performed by the invention. Alternately, the controlmicroprocessor may provide the search inquiry and template orprogramming instructions for the FPGA 21, and then perform the searchand present the data on system bus 34 for access and use by CPU 32.

As has been explained above, the invention may be used to perform avariety of different types of matching or data reduction operations onthe target data. Each one of these operations will now be discussed indetail below. For all operations, however, it will be assumed that thetarget data is written onto the magnetic mass storage medium 26 withsufficient formatting information attached so that the logical structureof the target data can be extracted. Exact and approximate stringmatching will be described with reference to FIGS. 2-5. It can beappreciated, however, that the invention is not limited to single stringmatches and is equally suitable for compound query matching (i.e.,queries involving a plurality of text strings having a certain logicalrelationship therebetween or which use Boolean algebra logic). Whenperforming an exact match with the re-configurable logic device 21 inthe analog domain, shown as Point A in FIG. 2, where matching is doneusing analog comparators and correlation techniques, an exact matchcorresponds to setting a sufficiently high threshold value for matchingthe data key with analog target data on the mass storage medium 26.Approximate matching in the analog domain corresponds to settingappropriate (lesser) threshold values. The success of an approximatematch may be determined by the correlation value set in there-configurable logic device 21 or by using one of a number ofmatching-performance metrics stored therein such as the number of bitswithin a data key that are equal to the corresponding bits in thescanned target data.

More particularly, a conventional rigid disk drive may have a pluralityof rotating disks with multiple transducers accessing each disk. Each ofthese transducers typically has its output feeding analog signalcircuitry 18, such as amplifiers. This is represented at point A. Asfurther shown in FIG. 2, typically the outputs of the analog circuitryare selectively provided to a single digital decoder 23 which thenprocesses one such output. This is represented at point B. This digitaloutput is typically then sent through error correction circuitry (ECC)25 and at its output C is then passed on to the bus 34 or disk cache 30.For purposes of the invention, it may be desirable to provide multipleparallel paths for target data by providing multiple digital decodersand ECC's. Exact matching in the digital domain could be performed atPoint B or Point C, which corresponds to the pre- andpost-error-corrected digital signal, respectively.

The results may be sent to a control microprocessor 22, which may or maynot be configured as part of an FPGA, to execute logic associated with acompound or complex search inquiry. In the most general case, a compoundsearch inquiry 40 will go through the transformation process illustratedin FIG. 3. In particular, the software system (not shown) that resideson the CPU 32 generates the search inquiry 40. This inquiry proceedsthrough a compiler 42, also located on the CPU 32, that is responsiblefor analyzing the search inquiry. There are three main results from thisanalysis: (1) determining the data key that will reside in the compareregisters within the re-configurable logic device 21; (2) determiningthe combining logic that must be implemented in the controlmicroprocessor 22; and (3) producing hardware description 44 in astandard hardware description language (HDL) format (or if possibleretrieving one from a library) that will be used to generate synthesiscommands 46 to the re-configurable logic device 21. Any commerciallyavailable HDL and associated compiler and synthesis tools may be used.The resulting logic functions may correspond to exact or inexact matchesor wildcard operations and simple word level logic operations such as“and” and “or.” This synthesis information is sent to the controlmicroprocessor 22 which acts to set up the re-configurable logic device21, or FPGA. In the case of complex logic operations, a high-levellanguage 48 such as C or C++ is used in conjunction with a compiler 50to generate the appropriate synthesis commands to the microprocessor 22.

While the path shown in FIG. 3 is able to handle a wide range ofpotential search inquiries, it has the drawback that the latencyintroduced into the search process might be too long. If the timerequired for a search inquiry to flow through the transformationsrepresented in FIG. 3 is of the same order as the time required toperform a search, the compilation process might become the performancebottleneck rather than the search itself. This issue can be addressedfor a wide range of likely search inquiries by maintaining a set ofprecompiled hardware templates that handle the most common cases. Thesetemplates may be provided and maintained either in CPU 32 memory, madeavailable through an off-line storage medium such as a CD, or even keptin the mass storage medium 26 itself. Still further, such templates maybe communicated to CPU 32 such as over a network or the Internet.

One embodiment of such a hardware template 29 is illustrated in FIG. 4.In particular, the data shift register 27 contains target data streamingoff the head (not shown) of one or more disks 19. A compare registerstores the data key for which the user wishes to match. In the exampleshown, the data key is “Bagdad.” Fine-grained comparison logic device 31performs element by element comparisons between the elements of the datashift register 27 and the compare register 35. The fine-grainedcomparison logic device 31 can be configured to be either case sensitiveor case insensitive. Word-level comparison logic 37 is responsible fordetermining whether or not a match at the world-level occurs. In thecase of a compound search inquiry, the word-level match signals aredelivered to the control microprocessor 22 for evaluation thereof. Amatch to the compound search inquiry is then reported to the CPU 32 forfurther processing.

One embodiment of a hardware template for conducting approximatematching is illustrated in FIG. 5. In particular, the data shiftregister 27′ contains target data streaming off the head (not shown) ofone or more disks 19′. A compare register 35′ stores the data key forwhich the user wishes to match. In the example shown, the data key isagain “Bagdad.” Fine-grained comparison logic 31′ performs element byelement comparisons between the elements of the data shift register 27′and the compare register 21′. Again, the fine-grained comparison logicdevice 31′ can be configured to be either case sensitive or caseinsensitive. The template 29′ provides for alternate routing of elementsin data shift register 27′ to individual cells of the fine-grainedcomparison logic device 21′. Specifically, each cell of the fine-grainedcomparison logic device 31′ can match more than one position in the datashift register 27′ such that the compare register 21′ can match both thecommonly used spelling of “Baghdad” as well as the alternate “Bagdad” inshared hardware. Word-level comparison logic 37′ is responsible fordetermining whether or not a match at the word level occurs. In the caseof a compound search inquiry, the word-level match signals are deliveredto the control microprocessor 22 for evaluation thereof. A match to thecompound search inquiry is then reported to the CPU 32 for furtherprocessing.

The actual configuration of the hardware template will of course varywith the search inquiry type. By providing a small amount of flexibilityin the hardware templates (e.g., the target data stored in the compareregisters, the routing of signals from the data shift registers andcompare register elements to the cells of the fine-grained comparisonlogic device, and the width of the word-level comparison logic), such atemplate can support a wide range of word matches. As a result, thisdiminishes the frequency with which the full search inquirytransformation represented in FIG. 3 must take place, which in turn,increases the speed of the search.

It should be noted that the data entries identified in an “approximate”match search will include the “exact” hits that would result from an“exact” search. For clarity, when the word “match” is used, it should beunderstood that it includes a search or a data result found througheither of an approximate search or an exact search. When the phrase“approximate match” or even just “approximate” is used, it should beunderstood that it could be either of the two searches described aboveas approximate searches, or for that matter any other kind of “fuzzy”search that has a big enough net to gather target data that are looselyrelated to the search inquiry or in particular, data key. Of course, anexact match is just that, and does not include any result other than anexact match of the search inquiry with a high degree of correlation.

Also shown in FIG. 1 is a network interface 36 interconnecting thepresent invention to a network 38 which may be a LAN, WAN, Internet,etc. and to which other computer systems 40 may be connected. With thisarrangement, other computer systems 40 may conveniently also access thedata stored on the mass storage medium 26 through the present invention21. More specific examples are given below. Still further as shown inFIG. 1, the elements 20-24 may themselves be packaged together and forma disk drive accelerator that may be separately provided as a retrofitdevice for adapting existing pc's having their own disk drives with theadvantages of the invention. Alternately, the disk drive accelerator mayalso be offered as an option on a hard drive and packaged in the sameenclosure for an external drive or provided as a separate pc board withconnector interface for an internal drive. Still further alternatively,the disk drive accelerator may be offered as an option by pc suppliersas part of a pc ordered by a consumer, business or other end user. Stillanother embodiment could be that of being offered as part of a largermagnetic mass storage medium, or as an upgrade or retrofit kit for thoseapplications or existing installations where the increased data handlingcapability could be used to good advantage.

As shown in FIGS. 6-8, the invention may be implemented in a variety ofcomputer and network configurations. As shown in FIG. 6, the inventionmay be provided as part of a stand-alone computer system 41 comprising aCPU 43 connected to a system bus 45 which then accesses a mass storagemedium 47 having the invention as disclosed herein.

As shown in FIG. 7, the mass storage medium 51 coupled with theinvention may be itself connected directly to a network 52 over which aplurality of independent computers or CPU's 54 may then access the massstorage medium 51. The mass storage medium 51 may itself be comprised ofa bank of hard disk drives comprising a RAID, disk farm, or some othermassively parallel memory device configuration to provide access andapproximate matching capabilities to enormous amounts of data atsignificantly reduced access times.

As shown in FIG. 8, a mass storage medium 56 coupled with the inventionmay be connected to a network 58 as a network attached storage device(NASD) such that over the network 58 a plurality of stand-alonecomputers 60 may have access thereto. With such a configuration, it iscontemplated that each mass storage medium, represented for illustrativepurposes only as a disk 57, would be accessible from any processorconnected to the network. One such configuration would include assigninga unique IP address or other network address to each mass storagemedium.

The configurations as exemplified by those shown in FIGS. 1 and 6-8represent only examples of the various computer and networkconfigurations with which the invention would be compatible and highlyuseful. Others would be apparent to those having skill in the art andthe present invention is not intended to be limited through the examplesas shown herein which are meant to be instead illustrative of theversatility of the present invention.

As shown in FIG. 9, the method of the invention for use in exact orapproximate matching is described alternatively with respect to whetheran analog or digital data domain is being searched. However, beginningat the start of the method, a CPU performs certain functions duringwhich it may choose to access target data stored in a mass storagemedium. Typically, the CPU runs a search inquiry application 62 whichmay be representative of a DNA search, an Internet search, an analogvoice search, a fingerprint search, an image search, or some other suchsearch during which an exact or approximate match to target data isdesired. The search inquiry contains directives specifying variousparameters which the disk control unit 28 and the re-configurable logicdevice 20 must have to properly obtain the data key from the massstorage medium 26. Examples of parameters include but are not limited tothe following: the starting location for scanning the storage device;the final location after which (if there is not match) scanning isterminated; the data key to be used in the scanning; a specification ofthe approximate nature of the matching; and what information should bereturned when a match occurs. The sort of information that can bereturned includes the address of the information where the match wasfound, or a sector, record, portion of record or other data aggregatewhich contains the matched information. The data aggregate may also bedynamically specified in that the data returned on a match may bespecified to be between bounding data specifiers with the matched datacontained within the bounding field. As the example in FIG. 5 shows,looking for the word “bagdad” in a string of text might find theapproximate match, due to misspelling, of the word “Baghdad”, and returna data field which is defined by the surrounding sentence. Another queryparameter would indicate whether the returned information should be sentto the system or input/output bus 34, or the disk cache 30.

Referring back to FIG. 9, the search inquiry will typically result inthe execution of one or more operating system utilities. As an exampleof a higher level utility command, for the UNIX operating system, thiscould be modified versions of glimpse, find, grep, apropos, etc. Thesefunctions cause the CPU to send commands 66 such as search, approximatesearch, etc., to the re-configurable logic device 21 with relevantportions of these commands also being sent to the disk controller 28 to,for example, initiate any mass storage medium positioning activity 69that is later required for properly reading target data from the massstorage medium.

At this point, depending upon the particular methodology desired to beimplemented in the particular embodiment of the invention, it would benecessary that an analog or digital data key is determined. This datakey, which can be either exact or approximate for a text search,corresponds to the data being searched for. For an analog data key, itmay either be pre-stored such as in the mass storage medium, developedusing dedicated circuitry, or required to be generated. Should theanalog data key be pre-stored, a send pre-stored data key step 68 wouldbe performed by the microprocessor 22 (see FIG. 1) which would transmitthe data key in digital and sampled format to the re-configurable logicdevice 20 as shown in step 70. Alternatively, should the analog data keynot be pre-stored, it can be developed using one of a number ofmechanisms, two of which are shown in FIG. 9. In one, the microprocessor22 would write the data key on the magnetic mass storage medium as atstep 72 and then next read the data key as at step 74 in order togenerate an analog signal representation of the data key. In another, asat step 71, the digital version of the data key received from the CPUwould be converted using appropriate digital to analog circuitry to ananalog signal representation which would in turn be appropriatelysampled. The data key would then next be stored as a digital samplethereof as in step 70. Should a digital data key be used, it is onlynecessary that the microprocessor 22 store the digital data key as atstep 76 in the compare register of the re-configurable logic device. Itshould be understood that depending upon the particular structuresdesired to be included for each re-configurable logic device, the datakey may reside in either or all of these components, it merely beingpreferable to ultimately get the appropriate digital format for the datakey into the re-configurable logic device 21 for comparison andcorrelation.

Next, after the mass storage medium 26 reaches its starting location asat 79, the target data stored on the mass storage medium is continuouslyread as at step 78 to generate a continuous stream signal representativeof the target data. Should an analog data key have been used, thisanalog data key may then be correlated with an analog read of the targetdata from the mass storage medium 26 as at step 80.

While the inventors contemplate that any of many prior art comparatorsand correlation circuitry could be used, for present purposes theinventors suggest that a digital sampling of the analog signal and datakey could be quite useful for performing such comparison and calculatingthe correlation coefficient, as explained below. It is noted that thisanalog signal generated from reading the target data from mass storagemedium 26 may be conveniently generated by devices in the prior art fromthe reading of either analog or digital data, it not being necessarythat a digital data key be used to match digital target data as storedin mass storage medium 26. Alternatively, a correlation step 82 may beperformed by matching the digital data key with a stream of digitaltarget data as read from the mass storage medium 26. It should be notedthat the data key may reflect the inclusion of approximate informationor the re-configurable logic device 21 may be programmed to allow forsame. Thus, correlating this with target data read from the mass storagemedium enables approximate matching capabilities.

Referring back to FIG. 9, decision logic 84 next makes an intelligentdecision as to whether a portion of the target data approximatelymatches or does not approximately match the data key. Should a match befound, then the target data is processed as at step 86 and the key datarequested by the search inquiry is sent to a disk cache 30, directlyonto system bus 34, or otherwise buffered or made available to a CPU 32,network interface 36, or otherwise as shown in FIGS. 1, and 6-8. Alogical step 88 is preferably included for returning to the continuousreading of target data from the mass storage medium 26, indicatingsomething like a “do” loop. However, it should be understood that thisis a continuous process and that target data is processed from the massstorage medium 26 as a stream and not in individualized chunks, frames,bytes, or other predetermined portions of data. While this is notprecluded, the present invention preferably allows a data key to be inessence “slid” over a continuously varying target data read signal suchthat there is no hesitation in reading target data from the mass storagemedium 26. There is no requirement to synchronize reading to the startor end of any multi-bit data structure, or any other intermediate stepsrequired to be performed as the target data is compared continuously “onthe fly” as it is read from the mass storage medium 26. Eventually, thedata access is completed as at step 90 and the process completed.

The inventors herein have preliminarily tested the present invention inthe analog domain and have generated preliminary data demonstrate itsoperability and effectiveness. In particular, FIG. 10 is a graphicalrepresentation of a measured analog signal output from a read/write headas the read/write head reads a magnetic medium on which is stored a10-bit digital data key. As shown therein, there are peaks in the analogsignal which, as known in the art, represents the true analog signalgenerated by a read/write head as target data is read from a magneticmedium such as a hard disk. The scales shown in FIG. 10 are volts alongthe vertical axis and tenths of microseconds along the horizontal axis.As shown in FIG. 11, an analog signal is generated, again by aread/write head, as target data is read from a pseudo-random binarysequence stored in a test portion of a magnetic medium. The read signaldoes not provide an ideal square wave output when examined at thislevel.

FIG. 12 is a graphical representation, with the horizontal scaleexpanded, to more specifically illustrate the overlap betweenapproximately two bits of the 8-bit data key and the corresponding twobits of target data found in the pseudo-random binary sequence encodedat a different location on the disk or magnetic medium.

FIG. 13 is a graphical representation of a correlation coefficientcalculated continuously as the comparison is made between the data keyand the continuous reading of target data from the hard disk. Thiscorrelation coefficient is calculated by sampling the analog signals ata high rate and using prior art signal processing correlationtechniques. One such example may be found in Spatial Noise Phenomena ofLongitudinal Magnetic Recording Media by Hoinville, Indeck and Muller,IEEE Transactions on Magnetics, Volume 28, no. 6, November 1992, thedisclosure of which is incorporated herein by reference. A prior exampleof a reading, comparison, and coefficient calculation method andapparatus may be found in one or more of one of the co-inventor's priorpatents, such as U.S. Pat. No. 5,740,244, the disclosure of which isincorporated herein by reference. The foregoing represent examples ofdevices and methods which may be used to implement the presentinvention, however, as mentioned elsewhere herein, other similar devicesand methods may be likewise used and the purposes of the inventionfulfilled.

As shown in FIG. 13, at approximately the point labeled 325, a distinctpeak is noted at approximately 200 microseconds which approaches 1 Volt,indicating a very close match between the data key and the target data.FIG. 10 is also illustrative of the opportunity for approximate matchingwhich is believed to be a powerful aspect of the invention. Lookingclosely at FIG. 13, it is noted that there are other lesser peaks thatappear in the correlation coefficient. Thus, if a threshold of 0.4 Voltswere established as a decision point, then not only the peak occurringwhich approaches 1 would indicate a match or “hit” but also another fivepeaks would be indicative of a “hit”. In this manner, a desiredcoefficient value may be adjusted or predetermined as desired to suitparticular search parameters. For example, when searching for aparticular word in a large body of text, lower correlation values mayindicate the word is present but misspelled.

FIG. 14 depicts the continuous calculation of a correlation coefficientbetween the same 8-bit data key but with a different target data set.Again, a single match is picked up at approximately 200 microsecondswhere the peak approaches 1 Volt. It is also noted that should a lowerthreshold be established additional hits would also be located in thetarget data.

As previously mentioned, the invention is also capable of performingsequence matching searches. With reference to FIG. 15, a table 38 isgenerated by the re-configurable logic device 20 to conduct such asearch. Specifically, p₁ p₂ p₃ p₄ represents the data key, p, or desiredsequence to be searched. While the data key of FIG. 15 only shows fourcharacters, this is for illustrative purposes only and it should beappreciated that a typical data key size for sequence searching is onthe order of 500-1000, or even higher. The symbols t₁, t₂, t₃ . . . t₉represent the target data, t, streaming off of the mass storage medium26. Again, while only nine (9) characters of such data are shown, itshould be appreciated that the typical size of the mass storage medium26 and thus the target data streaming off of it can typically be in therange of several billion characters. The symbols d_(i,j) represent theedit distance at position i in the data key and position j in the targetdata. It is assumed that the data key is shorter relative to the targetdata, although it is not required to be so. There may be a set of known(constant) values for an additional row (d0,j) and column (di,0) notshown in FIG. 15.

The values for di,j are computed by the re-configurable logic device 20using the fact that di,j is only a function of the following characters:(1) pi, (2) tj, (3) di−1,j−1, (4) di-1,j, and (5) di,j−1. This isillustrated in FIG. 15 with respect to the position d3,6 by showing itsdependency on the values of d2,5 and d2,6 and d3,5 as well as p3 and t6.In one embodiment, the values for di,j are computed as follows:

di,j=max[di,j−1+A; di−1,j+A; di−1,j−1+Bi,j],

where A is a constant and Bi,j is a tabular function of pi and tj. Theform of the function, however, can be quite arbitrary. In the biologicalliterature, B is referred to as the scoring function. In the populardatabase searching program BLAST, scores are only a function of whetheror not pi=tj. In other contexts, such as for amino acid sequences, thevalue of B is dependent upon the specific characters in p and t.

FIG. 16 shows one embodiment of a systolic array architecture used bythe invention to compute the values in the table 38 of FIG. 15. Thecharacters of the data key are stored in the column of data registers53, while the characters of the target data streaming off of the massstorage medium 26 are stored in the data shift registers 55. The valuesof di,j are stored in the systolic cells 59 which themselves arepreferably FPGA's.

The operation of the array of FIG. 16 will now be illustrated usingFIGS. 17 and 18. As shown in FIG. 17, in the first (i.e., combinational)part of the clock cycle of the system, the four underlined values arecomputed. For example, the new value d3,6 is shown to depend upon thesame five values illustrated earlier in FIG. 15. As shown in FIG. 18, inthe second (i.e., latch) part of the clock cycle, all the characters indi,j and tj are shifted one position to the right. A comparator 61 ispositioned at each diagonal cell of the d array and determines when thethreshold has been exceeded.

The sequence matching operation will now be described with reference toFIGS. 19-22 with respect to the following example:

key=axbacs

target data=pqraxabcstvq

A=1

B=2, if i=j

B=−2 if i=j

From these variables, the table of FIG. 19 is generated by there-configurable logic device 20. Assuming a pre-determined threshold of“8”, the re-configurable logic device 20 will recognize a match at d6,9.

A portion of the synthesis arrays representing the values present inFIGS. 16-18 for this example are shown in FIGS. 20-22, respectively. Amatch is identified by the re-configurable logic device 20 when thevalue on any row exceeds a predetermined threshold. The threshold is setbased on the desired degree of similarity desired between the data keyand the target data stored in mass memory device 26. For example, in thecase of an exact match search, the data key and target data must beidentical. The match is then examined by the CPU 32 via a tracebackoperation with the table of FIG. 19. Specifically a “snapshot” of thetable is sent to the CPU 32 at a predetermined time interval to assistin traceback operations once a match is identified. The interval ispreferably not too often to overburden the CPU 32, but not so infrequentthat it takes a lot of time and processing to recreate the table. Toenable the CPU 32 to perform the traceback operation, it must be able torecreate the d array in the area surrounding the entry in the table thatexceeded the threshold. To support this requirement, the systolic arraycan periodically output the values of a complete column of d (“asnapshot”) to the CPU 32. This will enable the CPU 32 to recreate anyrequired portion of d greater than the index j of the snapshot.

Many matching applications operate on data representing a twodimensional entity, such as an image. FIG. 23 illustrates a systolicarray 120 of re-configurable logic devices 20, preferably FPGA's, whichenables matches on two dimensional data. The individual cells 122 eachhold one pixel of the image for which the user is desiring to match (theimage key) and one pixel of the image being searched (the target image).For images of sufficiently large size, it is likely they will not allfit into one re-configurable logic chip 124. In such cases, a candidatepartitioning of cells to chips is shown with the dashed lines, placing arectangular subarray of cells in each chip 124. The number ofchip-to-chip connections can be minimized by using a subarray that issquare (i.e., same number of cells in the vertical and horizontaldimension). Other more complicated arrangements are shown below.

Loading of the target image into the array 120 is explained using FIG.24. Individual rows of each target image streaming off the mass magneticmedium 26, shown generally as point A, into the top row 130 of the arrayvia the horizontal links 134 connecting each cell. With such aconfiguration, the top row 130 operates as a data shift register. Whenthe entire row 130 is loaded, the row is shifted down to the next row132 via the vertical links 136 shown in each column. Once the entireimage is loaded into the array, a comparison operation is performed,which might require arbitrary communication between neighboring cells.This is supported by both the horizontal and vertical bi-directionallinks 126 and 128, respectively, shown in FIG. 23.

Although for simplicity purposes the individual bi-directional links 126and 128 are shown simply in FIGS. 23 and 24, FIG. 28 shows theflexibility for implementing a much more complex set of bi-directionallinks. As shown in FIG. 28, data may be communicated from a mass storagemedium 180 and be input to a first row of a plurality of cells 182, witheach cell of the first row having a direct link to the correspondingcell 184 below it in a second row of cells with a simple link 186, andso on throughout the array 188 of cells. Overlying the array 188 ofcells is a connector web 190 which provides direct connectivity betweenany two cells within the array without the need for transmission throughany intervening cell. The output of the array 188 is represented by thesum of the exit links 192 at the bottom of the array 188. It should beunderstood that each cell in the array may be comprised of an FPGA, eachone of which preferably has a re-configurable logic elementcorresponding to element 20 in FIG. 1, or any one of which may have are-configurable logic element 20 as well as a data shift register 24, orany one of which may have the entirety of re-configurable logic device21.

One embodiment for the individual cells of array 120 is illustrated inFIG. 25. The cell 140 includes a pixel register 142, LOADTi,j, whichcontains the pixels of the target image currently being loaded into thearray. A register, 144 CMPTi,j, contains a copy of the pixel register142 once the complete target image has been loaded. This configurationenables the last target image loaded to be compared in parallel with thenext target image being loaded, essentially establishing a pipelinedsequence of load, compare, load, compare, etc. A register 146, CMPPi,j,contains the pixels of the image key to be used for comparison purposes,and the compare logic 148 performs the matching operation betweenregister 144 and register 146. The compare logic 148 may include theability to communicate with the neighboring cells to the left, right,up, and down shown generally as 150, 152, 154, and 156, respectively, toallow for complex matching functions.

Another embodiment for the individual cells of array 120 of FIG. 23 isillustrated in FIG. 26. The cell 140 of FIG. 25 has been augmented tosupport simultaneous loading of the image key and the target image. Inparticular, the cell 160 includes the same components of the cell 140,but adds a new register 162, LOADPi,j, which is used to load the imagekey, and is operated in the same manner as register 142. With such aconfiguration, if one disk read head of the mass storage medium 26 ispositioned above the image key, and a second disk read head ispositioned above the target image, they can both flow off the disk inparallel and be concurrently loaded into the array 160.

The operation performed within the compare logic block can be anyfunction that provides a judgment as to whether or not there aresignificant differences between the target image and the image key. Anexample includes cross-correlations across the entire image orsub-regions of the image as described in John C. Russ, The ImageProcessing Handbook, 3^(rd) edition, CRC Press 1999, which isincorporated herein by reference.

The invention is also capable of performing data reduction searching.Such searching involves matching as previously described herein, butincludes summarizing the matched data in some aggregate form. Forexample, in the financial industry, one might want to search financialinformation to identify a minimum, maximum, and latest price of a stock.A re-configurable logic device for computing such aggregate datareductions is illustrated as 100 in FIG. 27. Here, a data shift register102 reads target data from a mass storage medium containing stock priceinformation. In the example shown, three data reduction searches areshown, namely calculating the minimum price, the maximum price, and thelatest price. As target data is fed into the data shift register 102,decision logic computes the desired data reduction operation. Inparticular, the stock price is fed to a minimum price comparator 110 andmaximum price comparator 112 and stored therein. Each time a stock priceis fed to comparator 110, it compares the last stored stock price to thestock price currently being fed to it and whichever is lower is storedin data register 104. Likewise, each time a stock price is fed tocomparator 112, it compares the last stored stock price to the stockprice currently being fed to it and whichever is higher is stored indata register 106. In order to compute the latest price, the stock priceis fed into a data register 108 and the current time is fed into acomparator 114. Each time a time value is fed into comparator 114, itcompares the last stored time with the current time and which ever isgreater is stored in data register 116. Then, at the end of the desiredtime interval for which a calculation is being made, the latest price isdetermined.

While data reduction searching has been described with respect to thevery simple financial example shown in FIG. 27, it can be appreciatedthat the invention can perform data reduction searching for a variety ofdifferent applications of varying complexity requiring suchfunctionality. The re-configurable logic device need simply beconfigured with the hardware and/or software to perform the necessaryfunctions

The ability to perform data reduction searching at disk rotationalspeeds cannot be under-estimated. One of the most valuable aspects ofinformation is its timeliness. People are growing to expect things atInternet speed. Companies that can quickly compute aggregate datareductions will clearly have a competitive advantage over those thatcannot.

Additionally, data processing operations other than searching andreduction may also be implemented on the re-configurable logic device21. As mentioned above, these operations are referred to herein as datamanipulation operations. Examples of data manipulation operations orsuboperations thereof that can be performed on a PLD 20 includeencryption, decryption, compression, and decompression operations. Thepreferred PLD 20 is an FPGA, even more preferably, a Xilinx FPGA.Further, still, any of these additional operations can be combined withsearching and/or reduction operations in virtually any manner to form amulti-stage data processing pipeline that provides additional speed,flexibility, and security. The complexity of each operation is alsovirtually limitless, bounded only by the resources of there-configurable logic device 21 and the performance requirements of apractitioner of the invention. Each processing operation can beimplemented in a single stage or in multiple stages, as may benecessary.

FIG. 29 illustrates a multi-stage data processing pipeline 200implemented within a re-configurable logic device 21 for a system asshown in FIG. 1. At least one stage in the pipeline 200 is implementedon a PLD. Each stage 202 of the pipeline 200 is configured to processthe data it receives according to its intended functionality (e.g.,compression, decompression, encryption, decryption, etc.), andthereafter pass the processed data either to the next stage in thepipeline, back to a prior stage, or to the control processor 204. Forexample, the first stage 202 in the pipeline 200 operates on datastreaming from a mass storage medium 26 and processes that dataaccording to its functionality. The data processed by stage 1 isthereafter passed to stage 2 for further processing, and so on, untilstage N is reached. After the data has passed through all appropriatestages 202, the result(s) of that processing can be forwarded to thecontrol processor 204 and/or the computer over system bus 34.

This exemplary pipeline 200 of FIG. 29 can also be replicated so that aseparate pipeline 200 is associated with each head on a disk system ofthe mass storage medium 26. Such a design would improve performanceassociated with performing parallel processing operations on multipledata streams as those streams are read out from the disk. If there areno other performance bottlenecks in the system, it is expected thatthroughput will increase linearly with the number of pipelines 200employed.

It should be noted that each stage need not necessarily be implementedon a PLD 20 within the re-configurable logic device 21. For example,some stages may be implemented in software on a processor (not shown) ordedicated hardware (not shown) accessible to the PLD 20. The exactdesign of each stage and the decision to implement each stage on a PLD20, in software, or in dedicated hardware such as an ASIC, will bedependent upon the associated cost, performance, and resourcesconstraints applicable to each practitioner's plans. However, byemploying pipelining entirely within a PLD 20 such as an FPGA, theprocessing throughput can be greatly increased. Thus, for a balancedpipeline (i.e., a pipeline where each stage has the same execution time)having no feedback paths, the increase in data throughput is directlyproportional to the number of stages. Assuming no other bottlenecks, asmentioned above, then with N stages, one can expect a throughputincrease of N. However, it should be noted that the multi-stage pipelinemay also utilize feedback between stages, which may be desirable forcertain operations (e.g., some encryption operations) to reduceimplementation cost or increase efficiency.

FIG. 30 illustrates an exemplary multistage pipeline 200 wherein thefirst four stages 202 comprise a decryption engine 210. The decryptionengine 210 in this example operates to receive encrypted and compresseddata streaming from the mass storage medium 26. The fifth stage 202serves as a decompression engine to decompress the decrypted compresseddata exiting the decryption engine 210. The output of the decompressionengine is thus a stream of decrypted and decompressed data that is readyto be processed by the stage 6 search engine. Control processor 204controls each stage to ensure proper flow therethrough. The controlprocessor 204 preferably sets up parameters associated with eachpipeline stage (including, if appropriate, parameters for stagesimplemented in software).

FIG. 31 depicts an example wherein a PLD is used as an encryption enginefor data either flowing from the system bus 34 to the mass storagemedium 26 or data flowing from the mass storage medium 26 to the systembus 34. FIG. 32 depicts yet another exemplary pipeline wherein thepipeline 200 is comprised of multiple processing engines (each enginecomprising one or more stages), each of which can be either activated bythe control processor 204 such that the engine performs its recited taskon the data it receives or deactivated by the control processor 204 suchthat is acts as a “pass through” for the data it receives.Activation/deactivation of the different engines will in turn depend onthe functionality desired for the pipeline. For example, if it isdesired to perform a search operation on encrypted and compressed datastored in the mass storage medium 26, the decryption engine 210,decompression engine 214, and search engine 218 can each be activatedwhile the encryption engine 212 and compression engine 216 can each bedeactivated. Similarly, if it is desired to store unencrypted data inthe mass storage medium in a compressed and encrypted format, thecompression engine 216 and the encryption engine 212 can be activatedwhile the decryption engine 210, the decompression engine 214, and thesearch engine 218 are each deactivated. As would be understood by thoseof ordinary skill in the art upon reading the teachings herein, otheractivation/deactivation combinations can be used depending on thedesired functionality for the pipeline 200.

Advanced encryption/decryption algorithms require a complex set ofcalculations. Depending on the particular algorithm employed, performingencryption/decryption at disk speed requires that one employ advancedtechniques to keep up with the streaming data arriving at theencryption/decryption engine. The PLD-based architecture of the presentinvention supports the implementation of not only relatively simpleencryption/decryption algorithms, but also complex ones. Virtually anyknown encryption/decryption technique can be used in the practice of thepresent invention, including but not limited to DES, Triple DES, AES,etc. See Chodowiec et al., “Fast Implementations of Secret-Key BlockCiphers Using Mixed Inter- and Outer-Round Pipelining”, Proceedings ofInternational Symposium on FPGAs, pp. 94-102 (February 2001); FIPS 46-2,“Data Encryption Standard”, revised version issued as FIPS 46-3,National Institute of Standards and Technology (1999); ANSI x9.52-1998,“Triple Data Encryption Algorithm Modes of Operation”, American NationalStandards Institute (1998); FIPS 197, “Advanced Encryption Standard”,National Institute of Standards and Technology (2001), the entiredisclosures of all of which are incorporated herein by reference.

FIG. 33 illustrates an example of single stage encryption that can beimplemented with the present invention. The data flow direction is topto bottom. A block of text (typically 64 or 128 bits) is loaded intoinput register 220 (by either control processor 204 or CPU 32).Combinational logic (CL) 224 computes the cipher round, with the resultsof the round being stored in output register 226. During intermediaterounds, the contents of output register 226 are fed back throughfeedback path 225 into the CL 224 through MUX 222 to compute subsequentrounds. Upon completion of the final round, the data in the outputregister is the encrypted block and is ready to be stored in the massstorage medium. This configuration can also be used as a single stagedecryption engine as well, wherein the CL that computes the cipher isdecryption logic rather than encryption logic.

The throughput of the encryption engine shown in FIG. 33 can be improvedthrough the use of pipelining techniques. FIG. 34 depicts an example ofa pipelined encryption engine wherein there is pipelining within thecombinational logic of the round itself. Each CL 224 includes multipleintra-round pipeline registers 228. The number of intra-round pipelineregisters 228 used can be variable and need not be limited to two perCL. Further, the loops represented by the feedback path 225 can beunrolled with multiple copies of the round CL 224 a, 224 b, . . . , eachwith an inter-round pipeline register 230 therebetween. As with thenumber of intra-round registers 228 for each CL 224, the degree ofunrolling (i.e., number of round CLs 224) is also flexible. Relative tothe encryption engine of FIG. 33, it should be noted that the engine ofFIG. 34 will consume more resources on the PLD 20, but will provide ahigher data throughput.

FIG. 35 illustrates an example of an encryption engine wherein therounds are completely unrolled. The feedback paths 225 of FIGS. 33 and34 are no longer necessary, and data can continuously flow from theinput register 220 through the pipeline of CLs 224 (each includingmultiple intra-round pipeline registers 228 and separated by inter-roundpipeline registers 230) to the output register 226. Relative to theencryption engines of FIGS. 33 and 34, this configuration provides thehighest data throughput, but also requires the greatest amount ofresources in the re-configurable logic.

In many situations, data is retained in a data warehouse, as shown inFIG. 36. The person or entity who owns the data warehouse (the actualhardware and related database technology on which data resides) is oftennot the same person or entity who owns the actual data stored therein.For example, if Party A (a data warehouser) owns a data warehouse andoffers data warehousing service to Party B (a data owner who is to useParty A's data warehouse to physically store data), then the data ownerhas a legitimate concern about the third parties who may have access tothe data stored in the data warehouser's warehouse. That is, the datawarehouser controls physical access to the data, but it is the dataowner who wants to control who may physically access the data through anaccess gateway, as shown in FIG. 36. In such cases, it is conventionalfor the data owner's data to be stored in the data warehouse in anencrypted format, and the data owner retains control over thedistribution of any decryption algorithm(s) and/or key(s) for the storeddata. That way, the risk of unauthorized third parties gaining access tothe unencrypted format of the data owner's data is reduced. In such anarrangement, the data warehouser is not provided with access to anunencrypted version of the data owner's stored data.

If the data owner wishes to communicate all or a portion of its storedencrypted data from the data warehouse to Party C via a network such asthe Internet, that data can be protected during delivery over thenetwork via another form of encryption (e.g., different algorithm(s)and/or different decryption key(s)). The data owner can then provideParty C with the appropriate algorithm(s) and/or key(s) to decrypt thedata. In this manner, the data owner and the authorized third party arethe only two parties who have access to the decrypted (plain text) data.However, the authorized third party will not be able to decrypt the dataowner's data that is still stored in the data warehouse because thatdata will possess a different mode of encryption than the data received.

Conventionally, the computations required to performencryption/decryption in data warehousing scenarios are performed insoftware on computers owned and under the direct control of the datawarehouser. In such a situation, as shown in FIG. 37, the plain textthat is the output of the decryption operation is stored in the mainmemory of the processor used to perform the encryption/decryptionoperations. If this software (or other software running on theprocessor) has been compromised by a virus or other malware, the dataowner may lose control over the plain text data to an unknown party.Thus, with conventional approaches, one or both of the data warehouserand an unknown malware-related party has access to the processor mainmemory, and therefore access to the plain text form of the data owner'sdata.

To improve upon this security shortcoming, the present invention can beused to implement encryption and decryption on re-configurable logicdevice 21 (preferably within a PLD 20) over which only the data ownerhas control, as shown in FIG. 38. In FIG. 38, a decryption engine 3800using Key 1 and an encryption engine 3802 using Key 2 are implemented ona PLD 20. The re-configurable logic device 21 remains under control ofthe data owner and preferably (although it need not be the case)communicates with the data store of the data warehouser over a networksuch as the Internet to receive a stream 3806 of the data owner'sencrypted data (wherein the stored data was previously encrypted usingKey 1). The decryption engine 3800 thus operates to decrypt the datastream 3806 using Key 1. The output 3804 of the decryption engine 3800is the data owner's data in decrypted (or plain text) format. This dataremains in the secure memory of the PLD 20 or the secure on-boardmemory. Because this secure memory is invisible and inaccessible tosoftware which may have malware thereon, the risk of losing control overthe plain text data to “hackers” is virtually eliminated. Thereafter,the plain text data 3804 is provided to encryption engine 3802, whichencrypts data 3806 using Key 2. The output of the encryption engine 3802is newly encrypted data 3808 that can be delivered to an authorizedthird party data requester. Secure delivery of data 3808 over a networksuch as the Internet can be thus maintained. For the authorized thirdparty data requester to interpret data 3808, the data owner can providethat third party with Key 2.

FIGS. 39( a) and (b) illustrate embodiments for this feature of thepresent invention. FIG. 39( a) illustrates a circuit board 3900 thatcould be installed in a computer server. PCI-X connector 3916 serves tointerface the board 3900 with the server's system bus 34 (not shown). APLD 20 such as an FPGA is implemented on board 3900. Within the FPGA,three functions are preferably implemented: a firmware socket 3908 thatprovides connection with the external environment, a decryption engine3904, and an encryption engine 3902. The FPGA preferably alsocommunicates with on-board memory 3906, which is connected only to theFPGA. A preferred memory device for on-board memory 3906 is an SRAM or aDRAM. The address space and existence of memory 3906 is visible only tothe FPGA. The FPGA is also preferably connected to a disk controller3912 (employing SCSI, Fiber Channel, or the like) via a private PCI-Xbus 3910. Disk connector 3914 preferably interfaces the disk controller3912 with mass storage medium 26 (not shown) which can serve as the datawarehouse. Disk controller 3912 and disk connector 3914 areoff-the-shelf components, well known in the art. Examples ofmanufacturers include Adaptec and LSI.

To support normal read/write access to the mass storage medium 26, theFPGA is preferably configured as a PCI-X to PCI-X bridge that links thePCI-X connector 3916 with the internal PCI-X bus 3910. These bridgingoperations are performed within firmware socket 3908, the functionalityof which is known in the art. Communication pathways other than PCI-Xmay be used, including but not limited to PCI-Express, PCI, Infiniband,and IP.

To support the encryption/decryption functionality, data streaming intothe board 3900 from the mass storage medium 26 is fed into thedecryption engine 3904. The plain text output of the decryption engine3904 can be stored in on-board memory 3906 (FIG. 39( a), stored inmemory internal to the FPGA (FIG. 39( b), or some combination of thetwo. Thereafter, the encryption engine 3902 encrypts the plain text datathat is stored in memory 3906, internal FPGA memory, or some combinationof the two, using a different key than that used to decrypt the storeddata. The choice of whether to use on-board memory 3906 or internal FPGAmemory will depend upon a variety of considerations, including but notlimited to the available FPGA resources, the volume of data to bedecrypted/encrypted, the type of decryption/encryption employed, and thedesired throughput performance characteristics.

During the time that the plain text is resident in the on-board memory3906 or in the internal FPGA memory, this plain text data is notaccessible to a processor accessing motherboard bus 34 because there isno direct connection between memory 3906 or internal FPGA memory and thePCI-X connector 3916. Accordingly, memory 3906 and the internal FPGAmemory are not in the address space of such a processor, meaning, byderivation, that memory 3906 and the internal FPGA memory are notaccessible by any malware that may be present on that processor.

Moreover, it should be noted that the embodiments of FIGS. 39( a) and(b) may also optionally include a search engine (not shown) within theFPGA located between the decryption engine 3904 and encryption engine3902, thereby allowing the data owner to deliver targeted subsets of thestored data to the authorized third party data requester that fit withinthe boundaries of the third party's data request.

As discussed above, compression and decompression are also valuableoperations that can be performed in a PLD in accordance with thetechniques of the present invention. It is common to compress data priorto storage in a mass storage medium 26 (thereby conserving storagespace), and then decompress that data when reading it from the massstorage medium for use by a processor. These conventional compressionand decompression operations are typically performed in software. Acompression technique that is prevalently used is the well-knownLempel-Ziv (LZ) compression. See Ziv et al., “A Universal Algorithm forSequential Data Compression”, IEEE Trans. Inform. Theory, IT-23(3):337-343 (1977); Ziv et al., “Compression of Individual Sequence viaVariable Rate Coding”, IEEE Trans. Inform. Theory, IT-24: 530-536(1978), the entire disclosures of both of which are incorporated byreference herein. Furthermore, the PLD-based architecture of the presentinvention supports the deployment of not only LZ compression but alsoother compression techniques. See Jung et al., “Efficient VLSI forLempel-Ziv Compression in Wireless Data Communication Networks”, IEEETrans. on VLSI Systems, 6(3): 475-483 (September 1998); Ranganathan etal., “High-speed VLSI design for Lempel-Ziv-based data compression”,IEEE Trans. Circuits Syst., 40: 96-106 (February 1993); Pirsch et al,“VLSI Architectures for Video Compression—A Survey”, Proceedings of theIEEE, 83(2): 220-246 (February 1995), the entire disclosures of all ofwhich are incorporated herein by reference. Examples of compressiontechniques other than LZ compression that can be deployed with thepresent invention include, but are not limited to, various losslesscompression types such as Huffman encoding, dictionary techniques, andarithmetic compression, and various known lossy compression techniques.

To improve the speed at which compressed data can be searched, it willbe valuable to also import the decompression operation onto the PLD 20that performs the searching, thereby providing the decompression withthe same speed advantages as the PLD-based search operation. FIG. 40illustrates this aspect of the present invention wherein a stream 4000of compressed data is passed from the mass storage medium 26 to are-configurable logic device 21 on which a decompression (expansion)engine 4002 and a search engine 4004 are implemented within a PLD 20.FIG. 41 illustrates a preferred embodiment for this aspect of theinvention. In FIG. 41, the FPGA 20 of board 3900 depicted in FIGS. 39(a) and (b) implements the decompression engine 4002 and the searchengine 4004. As described in connection with FIGS. 39( a) and (b), theintegrity of the plain text form of the stored data (the decompresseddata exiting the decompression engine 4002) is preserved because it isstored only in on-board memory 3906, internal FPGA memory, or somecombination of the two. FIG. 42 illustrates a preferred implementationfor a compression operation, wherein the FPGA 20 of board 3900 has acompression engine 4200 implemented thereon, thereby allowing datacoming from system bus 34 to be stored in a compressed manner on massstorage medium 26. As should be understood, the FPGA 20 of board 3900can also be loaded with the decompression engine 4002, search engine4004, and compression engine 4200. In such a deployment, depending onthe functionality desired of board 3900, either the compression engine4200 can be deactivated (thereby resulting in a combineddecompression/search functionality) or the decompression engine 4002 andsearch engine 4004 can both be deactivated (thereby resulting in acompression functionality).

To configure FPGA 20 with the functionality of the present invention,the flowchart of FIG. 43 is preferably followed. First, code level logic4300 for the desired processing engines that defines both the operationof the engines and their interaction with each other is created. Thiscode, preferably HDL source code, can be created using standardprogramming languages and techniques. As examples of an HDL, VHDL orVerilog can be used. Thereafter, at step 4302, a synthesis tool is usedto convert the HDL source code 4300 into a gate level description 4304for the processing engines. A preferred synthesis tool is the well-knownSynplicity Pro software provided by Synplicity, and a preferred gatelevel description 4304 is an EDIF netlist. However, it should be notedthat other synthesis tools and gate level descriptions can be used.Next, at step 4306, a place and route tool is used to convert the EDIFnetlist 4304 into the template 4308 that is to be loaded into the FPGA20. A preferred place and route tool is the Xilinx ISE toolset thatincludes functionality for mapping, timing analysis, and outputgeneration, as is known in the art. However, other place and route toolscan be used in the practice of the present invention. The template 4308is a bit configuration file that can be loaded into the FPGA 20 throughthe FPGA's Joint Test Access Group (JTAG) multipin interface, as isknown in the art.

As mentioned above, templates 4308 for different processingfunctionalities desired for the system can be pre-generated and storedfor selective implementation on the FPGA. For example, templates fordifferent types of compression/decompression, different types ofencryption/decryption, different types of search operations, differenttypes of data reduction operations, or different combinations of theforegoing can be pre-generated and stored by a computer system forsubsequent loading into the FPGA 20 when that functionality is needed.

Further still, performance characteristics such as throughout andconsumed chip resources can be pre-determined and associated with eachprocessing operation. Using these associated parameters, an algorithmcan be used to intelligently select which template is optimal for aparticular desired functionality. For example, such an algorithm couldprovide guidance as to which of the encryption engines of FIGS. 33-35 isbest suited for a given application. The table below presents parametersthat can be used to model performance in accordance with theencryption/decryption operations of the invention.

TABLE 1 Variable definitions. Variable Definition B size of a block(number of bits encrypted/decrypted at a time) R number of rounds inoverall operation (encryption/ decryption) L loop unrolling level,number of rounds concurrently exe- cuting in loop-level pipelining(loop-level pipelining depth) p pipelining depth within each roundf_(CLK)(p, L) achievable clock rate for given pipelining configurationT_(CLK)(p, L) period of clock = 1/f_(CLK)(p, L) I number of iterationsrequired for each block = [R/L] A_(R)(p) chip resources required for around with internal pipelining depth p (including inter-round pipeliningregister) A₀ chip resources required for fixed components (e.g., inputregister, mux., etc.)The values for each of these parameters are readily known or can bereadily measured, as known in the art. If R=IL for an integer I, theiterations for the encryption/decryption have been evenly unrolled. Ifthis is not the case, later pipeline stages must have a pass-throughcapability, as the final result would be computed inside the pipelinerather than at the end.

The throughput of a pipelined cipher engine is given by the followingexpression:

${Throughput} = \frac{{Bf}_{CLK}\left( {p,L} \right)}{I}$

The chip resources for an FPGA are typically measured in CLBs or slices,as is well-known. With re-configurable logic other than FPGAs, theresources might be measured in other units (e.g., chip area). In eitherevent, the resources required will be linear in the number of roundssupported in parallel. Hence, the chip resources required for the engineis as follows:

Resources=A ₀ +LA _(R)(p)

The values for the parameters Throughput and Resources can be determinedin advance for each stored processing operation (or function f_(i)) thatmay be implemented in a stage of a pipeline. Accordingly, a table can becreated that relates each processing operation or function with itscorresponding values for Throughput and Resources.

Accordingly, the specific template (which defines one or more differentprocessing operations) to be deployed on a PLD can be tailored to theparticular query or command issued. An algorithm that balancesThroughput and Resources in a manner desired by a practitioner of thepresent invention can be created to decide which candidate template isbest-suited for an application. Thus, a control processor 32 can computethe overall throughput and resources for a set of functions as follows.The throughput for a set of functions is the minimum throughput for eachof the functions:

Throughput=Min(Throughput_(F1),Throughput_(F2), . . . , Throughput_(Fn))

The resources required to deploy a set of functions is the sum of theresources required for each of the functions:

Resources=Resources_(F1)+Resources_(F2)+ . . . +Resources_(Fn)

Given several options for each function, the control processor can thensolve an optimization problem (or if desired a “near optimization”problem). The optimization can be to deploy the set of options for eachfunction that maximizes the overall throughput under the constraint thatthe required resources be less than or equal to the available resourceson the re-configurable logic, or the optimization can be to deploy theset of options for each function that minimizes the required resourcesunder the constraint the that overall throughput not fall below somespecified minimum threshold. Techniques for solving such optimizationproblems or near optimization problems are well known in the art.Examples of such techniques include, but are not limited to completeenumeration, bounded search, genetic algorithms, greedy algorithms,simulated annealing, etc.

The use of the inventive system to process data streaming from a massstorage medium such as a disk drive system is a powerful technique forprocessing stored data at high speeds. Very large databases, however,typically span many disk cylinders. Accordingly, delays may beencountered when database files are written on tracks that have beenplaced on non-contiguous disk cylinders. These delays are associatedwith having to move the disk read/write head from its current positionover a data cylinder to a new data cylinder where the file to be readfrom the disk continues. These delays increase as the distance that thehead must travel increases. Therefore, for reading data that spansmultiple data cylinders on the disk, the flow of the data stream fromthe disk will be interrupted as the head moves from cylinder tocylinder. With today's disk drives, these delays may be in themillisecond range. Thus, these head movement delays (known in the art as“seek” times) represent a potential performance bottleneck.

With standard contemporary disk systems, tracks 4400 are laid out on thedisk or sets of disk platters as cylinders 4402 that are concentricaround central origin 4406, as shown in FIGS. 44( a) and (b). FIG. 44(a) illustrates a rotatable planar magnetic medium 4450 that serves as astorage device such as a computer hard disk, wherein data is placed onthe magnetic medium 4450 in discrete, circular tracks 4400. In magneticrecordings, each track 4400 _(i), wherein i may be a, b, c, . . . , ispositioned at its own radius R_(i) relative to the central origin 4406.Each track is radially separated from the next inner track and the nextouter track by a track-to-track spacing T. The value of T is preferablyuniform for each track-to-track radial distance. However, this need notbe the case. For a head 4404 to read or write data from track 4400 _(i),the head 4404 must be positioned such that it resides over a point onthe disk that is R_(i) from the origin 4406. As the disk rotates, thetrack will pass under the head to allow for a read or write operation.

Disk drives typically utilize a direct overwrite approach, so accurateradial placement of the head 4404 over the medium 4450 is critical forsustained error free use. In general, each circular track 4400 _(i) isdivided into about 150 roughly equal contiguous arcs. FIG. 44( a)depicts an example wherein each track 4400 _(i) is divided into 8uniform contiguous arcs 4460, each arc 4460 spanning an angle of θ=2π/8.The arcs of different tracks 4400 that span the same angle θ comprise adisk sector (or wedge) 4462, as known in the art.

These arcs 4460 contain several data sets 4464 (logical blocks andphysical sectors) that can be altered (rewritten). Additionally, thesearcs 4460 contain unalterable (fixed) magnetically written markings 4466(such as ABCD servo bursts) that are used as a guide to place the head4404 over the data regions so that the signal strength from the magneticrecording is maximized.

FIG. 44( b) is a block diagram view of a disk drive system 4470 with across-sectional view of several disks 4450 residing in the drive system.As shown in FIG. 44( b), many drives systems 4470 utilize both sides ofa disk 4450, and may include several disks 4450 (or platters) that areconcentrically placed on a rotational device 4472 such as a spindlemotor. In such an arrangement, each disk surface (top surface 4452 andbottom surface 4454) is accessed by a different head 4404. Thecollection of circular tracks 4400 accessed by the separate heads 4404at a single radius R_(i) is referred to as a “data cylinder” 4402. Aband of adjacent data cylinders is called a zone.

Having separate cylinders 4402 requires the movement of the disk head4404 when going between cylinders 4402. To move between cylinders 4402,the positioning system 4474 must appropriately move heads 4404 alongline 4476, typically in increments of T. As one moves from innercylinders to outer cylinders, the circumference of the written trackincreases. For example, with reference to FIG. 44( a), the circumferenceof innermost track 4400 _(a) is 2πR_(a), and the circumference ofoutermost track 4400 _(d) is 2πR_(d). Given that R_(d) is greater thanR_(a), it likewise follows that the circumference of track 4400 _(d) isgreater than that of track 4400 _(a). Given these circumferentialdifferences, different zones may be defined to allow for differentlinear bit densities along the track, thereby yielding more data sectorsaround the cylinder 4402 for larger radii than those yielded by usingroughly constant linear data densities.

To write data spanning one or more tracks 4400, the head 4404 must berepositioned by the positioning system 4474 to another radius by atleast the center-to-center distance of adjacent tracks 4400. This motionrequires mechanical settling time (repositioning of the head 4404) andresynchronization time of the head 4404 to the cylinder 4402 (in time,downtrack). When moving the head a relatively long distance such as T,this settling time is significant. Together, these times may take, onaverage, half the revolution of the cylinder 4402, which is typicallyseveral milliseconds when moving from cylinder to cylinder. As mentionedabove, this time duration is often referred to as the “seek” time, andit can be a major performance bottleneck. Due to this bottleneck, datawrite/read bursts are generally limited to single tracks or cylinders.

According to a novel and unique feature of the preferred embodiment, atechnique is used to reposition the head 4404 to accommodate tracks laidout as discontiguous arcs. In a preferred embodiment, thesediscontiguous arcs are discontiguous circular arcs arranged in agenerally helical tracking pattern on the disk 4450, and the headpositioning system uses servo patterns, such as ABCD servo bursts,already present in conventional systems to appropriately position thehead. This technique can provide for written bursts in excess of a trackand up to an entire zone, wherein a single zone may encompass the entiredisk. While other servo patterns are possible, and are not excluded fromthe scope of this feature of the invention, an example will be givenusing the conventional ABCD system for servo patterns.

In contrast to conventional head motion where the goal of the servosystem is to position the head 4404 on a single radius to provide acircular track 4400, this novel and unique positioning method, as shownin FIG. 45, aims to position the head 4404 over a discrete arc 4500 inproportion to the angular position of the head 4404 around the disk4450, thereby accommodating a helical topology of the discontiguousarcs' magnetic pattern on the disk 4450.

With reference to FIG. 45, consider a single revolution of a disk 4450uniformly divided into W wedges (or sectors) 4462, wherein each wedge4462 spans an angle of 2π/W. W is the total number of wedges 4462 thatpass the head 4404 in a single revolution of the disk. In FIG. 45, thehead (not shown) can be positioned at any point along the x-axis to theleft of origin 4406. Each wedge 4462 can be assigned a wedge number w,wherein w can be any integer 1 through W. As the disk 4450 spins, theradial displacement of the head 4404 will be incremented an amount inproportion to the wedge number, w, by the linear ratio (w/W)*T, where Tis the conventional track-to-track (or cylinder-to-cylinder) distance orsome other distance.

As shown in FIG. 45, data will be written on the surface of disk 4450 ina piece-wise fashion, preferably a piece-wise helical fashion defined bya plurality of discontiguous circular arcs 4500. For each revolution ofthe disk in a preferred embodiment, the head 4404 will be positioned toencounter W discontiguous circular arcs 4500, each circular arc 4500spanning an angle of 2π/W. In the example of FIG. 45, W is equal to 4.When it is stated that each arc 4500 is circular, what is meant is thateach arc 4500 _(i) possesses a substantially constant curvature. In apreferred embodiment wherein W is constant for all radii, eachdiscontiguous arc 4500 _(i) will possess a circumference of 2πR_(i)/W.The radius R_(i) for each arc 4500 _(i) is preferably T/W greater thanthat of arc 4500 _(i−1) and is preferably T/W less than that of arc 4500_(i+1). Thus, as noted below, for each complete revolution of the disk4450 in the preferred embodiment, the head 4404 will effectively move adistance equal to the conventional adjacent track-to-track distance T.As can be seen in FIG. 45, the plurality of discrete circular arcs 4500define a generally helical or spiral pattern on the disk 4450.

It should be noted that each radius R_(i) can have its own W value. Insuch cases, the discontiguous arcs 4500 may have differentcircumferences and may span multiple angles from the origin.

Each discontiguous arc 4500 will include an ABCD servo pattern thereonlike that shown in FIG. 44( a) for a contiguous arc to ensure propermovement of the head 4404 from one arc 4500 to the next. Conventionalservo systems have sufficient bandwidth to step heads 4404 by thesesmall amounts of T/W.

As part of this process, consider an example where the read/write head4404 is initially placed at position d₀ relative to central origin 4406for the disk of FIG. 45. This initial position can be R₁, the radialdistance of the innermost arc 4500 ₁. As the disk spins, for eachrevolution r, the radial displacement D of the head 4404 will bepositioned relative to d₀ by an amount proportional to the wedge numberwas follows:

$D = {\frac{rwT}{W} + d_{0}}$

wherein T is the conventional track-to-track (or cylinder-to-cylinder)distance. In one full revolution, the head 4404 will have radially movedexactly one full track-to-track distance T. When r reaches 2, the head4404 will have radially moved exactly 2T.

FIG. 46 illustrates the process by which a disk drive system 4470operates to read data from a disk 4450 in accordance with this featureof the preferred embodiment. At step 4600, the system senses the portionof the disk over which the head resides. Preferably, this step isachieved at least in part by sensing a servo pattern and reading asector ID written on the disk, as is known in the art. Thereafter, atstep 4602, depending on the wedge number w of the disk wedge 4502 thatthis portion corresponds to, the head is repositioned to D as each newdisk wedge 4502 is encountered by the head. Next, at step 4604, the headposition is fine-tuned using the servo pattern on the arc 4500. Once thehead is properly positioned, the data is read from the disk at step4606. The process then returns to step 4600 as the disk continues tospin.

This feature of the invention allows for the seamless and continuousoperation of the head in read or write mode over an entire zone, thuspermitting the reading or writing of an entire disk without incurringthe delays associated with normal seek times. Thus, when used incombination with the searching and processing techniques describedabove, a searching/processing system can operate more efficiently,without being stalled by seek time delays. However, it is worth notingthat this feature of the invention need not be used in combination withthe searching/processing techniques described above. That is, thistechnique of using a helical pattern to read and write data to and frommagnetic data storage disks can be used independently of theabove-described searching and processing features.

Another performance bottleneck occurs when a disk upon which data isstored becomes fragmented. In general file systems, the files aredivided into number of fixed size segments (blocks) and these segmentsare stored on the disk. If the file is very long, the segments might bestored at various locations on the disk. As noted above, to access sucha file the disk head has to move from cylinder to cylinder slowing downthe file access. It would be better if the entire file is stored as asingle object, in a single cylinder or immediately adjacent cylinders.However, this might not always be possible because of the fragmentationof the disk over time. The defragmentation of the disk usually involvesmoving all the files to one end of the disk so that the new files can beallocated contiguously on the other free end. Typically, such adefragmentation takes a long time. Many attempts have been made in theprior art to solve this problem. One well-known technique is known asthe binary buddy system. With the binary buddy system, every requestsize for disk space is rounded to the next power of 2. Thus, for a 2000byte file, an allocation request of 2048 (2¹¹) is made. This processleads to internal fragmentation.

In an effort to minimize these problems, disclosed herein is a techniquewhere a file is divided into one or more segments, wherein each segmentis a power of 2. Thus, each file that is not sized as an even power of 2is represented as the sum of a series of power of 2 segments.

In an embodiment wherein a minimum segment size is not set, thistechnique for segmenting a file into blocks of memory comprises: (1) ifthe file size is an even power of 2, requesting a block of storage spaceon the storage medium equal to the file size, (2) if the file size isnot an even power of 2, requesting a plurality of blocks of storagespace on the storage medium, each block having a size that is equal to apower of 2, and (3) if the request is accepted, storing the data file ina storage medium such as on a disk or in memory as one or more data filesegments in accordance with the request. In a preferred version of thistechnique, the file size F can be thought of in binary terms as F equalsF_(k) . . . F₂ F₁. When the file size is not an even power of 2,requesting blocks in storage comprises requesting a total number n ofblocks B₁, . . . , B_(n) equal to a total number of bits in F equal to1, each block B_(i) corresponding to a different bit F_(i) in F equal to1 and having a size of 2^(i). FIG. 47( a) illustrates an example of thisprocess for a file size F of 2500 bytes. As shown in FIG. 47( a), thepreferred sum of powers of 2 technique, wherein a minimum segment sizeis not used, results in segment sizes of 2048 bytes (2¹²), 256 bytes(2⁹), 128 bytes (2⁸), 64 bytes (2⁷) and 4 bytes (2²).

To avoid generating overly small segments, it is preferred that aminimum segment size 2 be used. For example, the minimum segment sizecan be 512 bytes (2⁹) (thus m is 2). With this technique, when a minimumsegment size is used, dividing a file into a sum of powers of 2 sizewill result in the smallest segment being at least equal to the minimumsegment size. Accordingly, (1) if the file size is an even power of 2and greater than or equal to 2^(m), then a block of storage space isrequested such that the block is equal to the file size, (2) if the filesize is less than 2^(m), then a block of storage space is requested suchthat the block is equal to 2^(m), and (3) if the file size is not aneven power of 2 and greater than 2^(m), then a plurality of blocks ofstorage space on the storage medium are requested, each block having asize that is equal to a power of 2 and equal to or greater than 2^(m).

FIG. 47( b) illustrates a preferred implementation of this minimumsegment feature, wherein the file size S is 2500 bytes. With thistechnique, it can be seen that the segment sizes will be 2048 bytes(2¹²), 512 bytes (2¹⁰). In the preferred implementation of FIG. 47( b),because at least one bit F_(i) in F_(m−1) through F₁ is equal to 1, thenF becomes rounded up to a new value R (which can be represented inbinary as R_(q) . . . R₂R₁). The value of R is chosen as the minimumvalue greater than F for which the bits R_(m−1) through R₁ are all equalto zero. If the file size F was a different value such that all of thebits F_(m−1) through F₁ are equal to zero, then the choice of blockswould proceed as with FIG. 47( a). However, if at least one of the bitsF_(m−1) through F₁ is equal to one, then the procedure of FIG. 47( b)using R is preferably followed.

As would be understood by those of ordinary skill in the art uponreviewing the teachings herein, program logic to implement such a sum ofpowers of 2 file system, with either a minimum segment size or without,can be readily developed.

With a sum of powers of 2 file system, the internal fragmentation isequal to conventional (usual) file systems, which divide a file intosegments of equal size, with the same minimum segment size. FIG. 48shows the wasted space due to internal fragmentation in a buddy filesystem versus a usual (conventional) system and a sum of powers of 2file system. When the minimum segment size is small, the wasted space issubstantial in the case of the buddy system, but it becomes comparableto other systems as the minimum segment size increases. As the number ofsmall files dominate in many file systems, the buddy system is oftentimes not a suitable option.

FIG. 49 compares the total number of segments, for an entire file,according to a usual file system and the sum of powers of 2 file system.When the minimum segment size is small, the sum of powers of 2 systemproduces significantly fewer segments than the usual mechanism. FIG. 50shows the minimum, average and maximum number of segments per fileaccording to both file systems. Here again, the sum of powers of 2 filesystem dominates and creates a low number of segments. In other words,the sum of powers of 2 file system leads to more contiguous files.

As such, the sum of powers of 2 file system is a good trade off betweenthe buddy system (where there is a lot of internal fragmentation) andthe usual file system (where there is less internal fragmentation butpotentially poor contiguity).

As a further refinement, it is preferred that a defragmentationalgorithm be used with the sum of powers of 2 file system to moregreatly ensure contiguous space on the disk for an allocation request.If a contiguous allocation cannot be satisfied, the defragmentationalgorithm tries to free space so as to satisfy the allocation request.This defragmentation algorithm does not defragment the entire disk.Instead, it incrementally defragments a portion of the disk to enablethe new allocation request to be satisfied in an incremental manner. Apreferred defragmentation algorithm for use with the sum of powers of 2file system is disclosed on pages 26-30 of the paper Cholleti, Sharath,“Storage Allocation in Bounded Time”, MS Thesis, Dept. of ComputerScience and Engineering, Washington University, St. Louis, Mo. (December2002), available as Washington University technical report WUCSE-2003-2,the entire disclosure of which is incorporated herein by reference.

Pseudo code for the preferred partial defragmentation algorithm,referred to herein as a “heap manager partial defragmentation algorithm”is reproduced below:

1. Initialization( ) for I = 0 to H−1  heapManager[i] = 0;/*empty heap*/2. Allocate(S) if there is a free block of size S  allocate the block ofsize S with the lowest address, A  UpdateHeapManager(S, A, “allocation”)else search for a free block of size bigger than S in increasing orderof size  if found, select the block with the lowest address   split theblock recursively until there is a block    of size S   select the blockof size S with the lowest address, A   UpdateHeapManager(S, A,“allocation”)  else   A = FindMinimallyOccupiedBlock(S) /*finds block to   relocate*/   Relocate(S, A) /*relocates the sub blocks from    blockA*/   allocate the block with address A   UpdateHeapManager(S, A,“allocation”) 3. FindMinimallyOccupiedBlock(S) find i such thatheapManager[i] is minimum for i = 2H/S−1 to  H/S return address A = i <<log₂S 4. Relocate(S, A)  subBlocks = FindSubBlocks(S, A);  for each SB εsubBlocks   Deallocate(SB), ∀SB ε subBlocks 5. Deallocate(extId) findaddress A of bock extId and size S; free the block; UpdateHeapManager(S,A, “deallocation”); 6. UpdateHeapManager(S, A, type) int maxLevel =log₂H; int level = log₂S; if type = “allocation”  int addr = A >> level; if S > MinBlockSize   heapManager[addr] = S /*block is fully occupied*/ /*blocks above the allocation level*/  addr = A >> level;  for (i =level+1; i <= maxLevel;i++)   addr = addr >> 1;   heapManager[addr] =heapManager[addr] + S; if type = “deallocation”  int addr = A >> level; /*current block*/  if S > MinBlockSize   heapManager[addr] = 0 /*blocks above the deallocation level*/  addr = A >> level;  for (i =level+1; i <= maxLevel;i++)   addr = addr >> 1; //continuing from aboveaddr   heapManager[addr] = heapManager[addr] − S;

Various changes and modifications to the present invention would beapparent to those skilled in the art but yet which would not depart fromthe spirit of the invention. The preferred embodiment describes animplementation of the invention but this description is intended to bemerely illustrative. Several alternatives have been also been above. Forexample, all of the operations exemplified by the analog processing havetheir equivalent counterparts in the digital domain. Thus, approximatematching and correlation types of processing can be done on the standarddigital representation of the analog bit patterns. This can also beachieved in a continuous fashion using tailored digital logic,microprocessors and digital signal processors, or alternativecombinations. It is therefore the inventors' intention that the presentinvention be limited solely by the scope of the claims appended hereto,and their legal equivalents.

1. A data processing apparatus comprising: a processing device forcommunicating with a computer system to offload a plurality ofprocessing tasks from a processor within the computer system, whereinthe processing device comprises a re-configurable logic device; whereinthe re-configurable logic device is configured to receive and processstreaming data through a multi-functional pipeline deployed on there-configurable logic device; wherein the multi-functional pipelinecomprises a plurality of pipelined data processing engines, eachpipelined data processing engine being configured to (1) perform aprocessing operation on streaming data that it receives, and (2) beresponsive to a control instruction that defines whether that pipelineddata processing engine is an activated data processing engine or adeactivated data processing engine, wherein an activated data processingengine is configured to perform its processing operation on any receivedstreaming data, and wherein a deactivated data processing engine remainsin the pipeline but does not perform its processing operation on anyreceived streaming data, the multi-functional pipeline thereby beingconfigured to provide a plurality of different pipeline functions inresponse to control instructions that are configured to selectivelyactivate and deactivate the pipelined data processing engines, eachpipeline function being the combined functionality of each activatedpipelined data processing engine in the pipeline at a given time; andwherein the pipelined data processing engines comprise a data reductionengine.
 2. The apparatus of claim 1 wherein the data reduction engine isconfigured to process streaming financial information to generatesummarized financial information.
 3. The apparatus of claim 2 whereinthe streaming financial information comprises data representative of aplurality of stocks and their associated prices, wherein the datareduction engine comprises a matching stage and a downstreamsummarization stage, wherein the matching stage is configured to searchwithin the streaming financial information to find matching stocks ofinterest with respect to at least one data key, and wherein thesummarization stage is configured to summarize the matching stocks in anaggregate form.
 4. The apparatus of claim 3 wherein the summarizationstage is configured to compute a minimum price for the stock prices of amatching stock found by the search stage.
 5. The apparatus of claim 4wherein the summarization stage comprises: a data shift register; a dataregister configured to store a most recent minimum stock price; and acomparator; wherein the summarization stage is further configured tostream the stock prices for the matching stock through the data shiftregister; wherein the comparator is configured to compare a currentstock price in the data shift register with the stored most recentminimum stock price to determine which is lower; and wherein thesummarization stage is further configured to update the stored mostrecent minimum stock price in the data register with the current stockprice in response to a determination by the comparator that the currentstock price is lower.
 6. The apparatus of claim 3 wherein thesummarization stage is configured to compute a maximum price for thestock prices of a matching stock found by the search stage.
 7. Theapparatus of claim 6 wherein the summarization stage comprises: a datashift register; a data register configured to store a most recentmaximum stock price; and a comparator; wherein the summarization stageis further configured to stream the stock prices for the matching stockthrough the data shift register; wherein the comparator is configured tocompare a current stock price in the data shift register with the storedmost recent maximum stock price to determine which is higher; andwherein the summarization stage is further configured to update thestored most recent maximum stock price in the data register with thecurrent stock price in response to a determination by the comparatorthat the current stock price is higher.
 8. The apparatus of claim 3wherein the streaming financial information further comprises datarepresentative of an associated time for each stock price, and whereinthe summarization stage is configured to compute a latest price for thestock prices of a matching stock found by the search stage.
 9. Theapparatus of claim 8 wherein the summarization stage comprises: a datashift register; a first data register configured to store a most recentlatest stock price; a second data register configured to store a mostrecent time; and a comparator; wherein the summarization stage isfurther configured to stream the stock prices and associated times forthe matching stock through the data shift register; wherein thecomparator is configured to compare a current time in the data shiftregister with the stored most recent time to determine which is later;and wherein the summarization stage is further configured to update thestored most recent latest stock price in the data register with thecurrent stock price in response to a determination by the comparatorthat the current time is later.
 10. The apparatus of claim 3 wherein thestreaming financial information further comprises data representative ofan associated time for each stock price, and wherein the summarizationstage is configured to simultaneously compute a minimum price, a maximumprice and a latest price for the stock prices of a matching stock foundby the search stage.
 11. The apparatus of claim 10 wherein thesummarization stage comprises: a data shift register; a first dataregister configured to store a most recent minimum stock price; a seconddata register configured to store a most recent maximum stock price; athird data register configured to store a most recent latest stockprice; a fourth data register configured to store a most recent time; afirst comparator; a second comparator; and a third comparator; whereinthe summarization stage is further configured to stream the stock pricesand times for the matching stock through the data shift register;wherein the first comparator is configured to compare the current stockprice in the data shift register with the stored most recent minimumstock price to determine which is lower; and wherein the summarizationstage is further configured to update the stored most recent minimumstock price in the first data register with the current stock price inresponse to a determination by the first comparator that the currentstock price is lower; wherein the second comparator is configured tocompare the current stock price in the data shift register with thestored most recent maximum stock price to determine which is higher;wherein the summarization stage is further configured to update thestored most recent maximum stock price in the second data register withthe current stock price in response to a determination by the secondcomparator that the current stock price is higher; wherein the thirdcomparator is configured to compare the current time in the data shiftregister with the stored most recent time to determine which is later;and wherein the summarization stage is further configured to update thestored most recent latest stock price in the third data register withthe current stock price in the data shift register in response to adetermination by the third comparator that the current time is later.12. The apparatus of claim 3 further comprising: a data store incommunication with the re-configurable logic device, the data storebeing configured to at least temporarily store the financialinformation; and wherein the re-configurable logic device is furtherconfigured to read the financial information from the data store tothereby receive the financial information stream.
 13. The apparatus ofclaim 12 wherein the data store comprises a mass storage medium.
 14. Theapparatus of claim 13 wherein the mass storage medium comprises amagnetic storage device.
 15. The apparatus of claim 3 wherein thematching stage and the summarization stage are configured to performtheir respective operations on different fields of the streamingfinancial information.
 16. The apparatus of claim 3 wherein the matchingstage is configured return, in response to finding a match with respectto the data key, a portion of the streaming financial information withina bounding field that encompasses the matching stock for processing bythe summarization stage.
 17. The apparatus of claim 3 wherein thematching engine comprises: a compare register, the compare registerhaving a plurality of cells configured to store elements of the datakey; a data shift register, the data shift register having a pluralityof cells configured to store elements of the streaming financialinformation, wherein each data shift register cell has a correspondingcompare register cell; a fine-grained comparison logic device, thefine-grained comparison logic device being configured to perform anelement-by-element comparison as between the financial informationelements and data key elements stored within corresponding ones of thedata shift register cells and compare register cells; word-levelcomparison logic, the word-level comparison logic being configured tofind the matching stocks based on the element-by-element comparison fromthe fine-grained comparison logic device; and wherein the data shiftregister is configured to continuously shift elements of the financialinformation from one data shift register cell to the next.
 18. Theapparatus of claim 17 wherein the fine-grained comparison logic devicecomprises a plurality of cells, each fine-grained comparison logic cellbeing in communication with a compare register cell and at least onedata shift register cell, and wherein the matching stage is furtherconfigured to route financial information elements from at least one ofthe data shift register cells to a plurality of different fine-grainedcomparison logic cells as the elements of the streaming financialinformation shift from one cell to the next within the data shiftregister to thereby support approximate matching.
 19. The apparatus ofclaim 2 wherein the pipeline is configured to process the streamingfinancial information on a frame-by-frame basis.
 20. The apparatus ofclaim 2 wherein the pipeline is configured to process the streamingfinancial information on a frameless basis.
 21. The apparatus of claim 2wherein the re-configurable logic device comprises a field programmablegate array (FPGA).
 22. The apparatus of claim 2 further comprising thecomputer system.
 23. The apparatus of claim 22 further comprising asystem bus, the system bus interconnecting the computer system with theprocessing device.
 24. The apparatus of claim 22 wherein the pipelineddata processing engines further comprise a decryption engine.
 25. A dataprocessing method comprising: receiving a plurality of controlinstructions at a processing device, the processing device incommunication with a computer system over a bus, wherein the processingdevice comprises a re-configurable logic device configured to receiveand process streaming data through a multi-functional pipeline deployedon the re-configurable logic device, the multi-functional pipelinecomprising a plurality of pipelined data processing engines, eachpipelined data processing engine being configured to (1) perform aprocessing operation on streaming data that it receives, and (2) beresponsive to a control instruction that defines whether that pipelineddata processing engine is an activated data processing engine or adeactivated data processing engine, wherein an activated data processingengine is configured to perform its processing operation on any receivedstreaming data, and wherein a deactivated data processing engine remainsin the pipeline but does not perform its processing operation on anyreceived streaming data, the multi-functional pipeline thereby beingconfigured to provide a plurality of different pipeline functions inresponse to control instructions that are configured to selectivelyactivate and deactivate the pipelined data processing engines, eachpipeline function being the combined functionality of each activatedpipelined data processing engine in the pipeline at a given time,wherein the pipelined data processing engines comprise a data reductionengine; and responsive to the received control instructions: activatingthe data reduction engine, and the activated data reduction engineprocessing streaming data to generate summarized data.
 26. The method ofclaim 25 wherein the streaming data comprises streaming financialinformation, and wherein the processing step comprises the activateddata reduction engine processing the streaming financial information togenerate summarized financial information.
 27. The method of claim 26wherein the streaming financial information comprises datarepresentative of a plurality of stocks and their associated prices,wherein the data reduction engine comprises a matching stage and adownstream summarization stage, and wherein the processing stepcomprises (1) the matching stage searching within the streamingfinancial information to find matching stocks of interest with respectto at least one data key, and (2) the summarization stage summarizingthe matching stocks in an aggregate form.
 28. The method of claim 27wherein the summarizing step comprises the summarization stage computinga minimum price for the stock prices of a matching stock found by thesearching step.
 29. The method of claim 27 wherein the summarizing stepcomprises the summarization stage computing a maximum price for thestock prices of a matching stock found by the searching step.
 30. Themethod of claim 27 wherein the streaming financial information furthercomprises data representative of an associated time for each stockprice, and wherein the summarizing step comprises the summarizationstage computing a latest price for the stock prices of a matching stockfound by the searching step.
 31. The method of claim 27 wherein thestreaming financial information further comprises data representative ofan associated time for each stock price, and wherein the summarizingstep comprises the summarization stage simultaneously computing aminimum price, a maximum price and a latest price for the stock pricesof a matching stock found by the searching step.
 32. The method of claim27 wherein the searching step and the summarizing step operate ondifferent fields of the streaming financial information.
 33. The methodof claim 26 wherein the method further comprises receiving the streamingfinancial information from a data source.
 34. The method of claim 33wherein the data source comprises a magnetic storage device, and whereinthe receiving step comprises reading the financial information from themagnetic storage device to thereby receive the financial informationstream.
 35. The method of claim 26 wherein the processing step comprisesthe data reduction engine processing the streaming financial informationon a frame-by-frame basis.
 36. The method of claim 26 wherein theperforming step comprises the data reduction engine processing thestreaming financial information on a frameless basis.
 37. The method ofclaim 26 wherein the re-configurable logic device comprises a fieldprogrammable gate array (FPGA).
 38. The method of claim 37 wherein theprocessing step comprises the data reduction engine generating thesummarized financial information at hardware speeds.
 39. A dataprocessing method comprising: within a computer system comprising aprocessor and a re-configurable logic device operating under control ofthe processor, streaming data through the re-configurable logic devicefor processing thereby, the re-configurable logic device comprising amulti-functional pipeline, the multi-functional pipeline comprising acontrol processor and a plurality of pipelined data processing engines,each of the pipelined data processing engines in the pipeline beingconfigured to (1) receive streaming data and (2) perform a dataprocessing operation on the received streaming data, wherein thepipelined data processing engines comprise a data reduction engine; thecontrol processor selectively activating and deactivating the pipelineddata processing engines in the pipeline to achieve a desired pipelinefunction, each pipeline function being the combined functionality of theactivated pipelined data processing engines in the pipeline, wherein theselectively activating and deactivating step comprises the controlprocessor activating the data reduction engine; and the activated datareduction engine processing streaming data to generate summarized dataat hardware speeds.
 40. The method of claim 39 wherein the streamingdata comprises streaming financial information, and wherein theprocessing step comprises the activated data reduction engine processingthe streaming financial information to generate summarized financialinformation at hardware speeds.